FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION (See Section 10 and Rule 13)
Title of invention:
METHOD AND SYSTEM FOR SWOT ANALYSIS OF RESEARCH AREA FROM UNSTRUCTURED DATA USING TECHNOLOGY
LANDSCAPING
Applicant
Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India
Preamble to the description
The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD [001] The embodiments herein generally relate to field of data analysis of unstructured data to derive insights and, more particularly, to a method and system for Strength Weakness Opportunity and Threat (SWOT) analysis of a research area from unstructured data using technology landscaping.
BACKGROUND
[002] Research area forecasting has become an indispensable part of every unit within an organization to aid its accelerated success. In each of the research area and its related subareas, there would be multitude of topics with each having its own area of specialization and impact on business. Research teams working in a specific research area need to pursue research in the most relevant topics of the day to establish a name and stay ahead of the competition. When an organization or an enterprise is seriously invested in a research area or a research sub area, it requires to perform Strength Weakness Opportunity and Threat (SWOT) analysis during its regular strategy planning exercise. Due to lack of insightful information, existing SWOT analysis approaches are often based on ad hoc information and imprecise methods, the results may not be dependable. As organizations are increasingly spending on research and research is becoming a major focus area for technology-driven organizations, a tool that can deliver SWOT analysis at a research area level consistently, transparently and accurately will be an important game changer and will also usher in better governance of research.
[003] Existing methods and tools providing SWOT analysis or similar insights generally indicate broad trends based on primarily bibliometrics. However, what is desired is an analysis that is tailored to the work being done in a specific research area or sub area in the context of developments at large. However, tools that can identify strengths and weaknesses in an organizations research portfolio, based on its current and past activity are not available. The challenge faced in providing SWOT analysis is that a research establishment's division of work into research areas and sub areas within each research area is often based on

organization's needs and available expertise and therefore they seldom conform any standard. Being able to accurately decompose the work of an organization to enable comparison with global activity and competitors’ activity is a big challenge. Tracking micro trends and spotting contextually relevant opportunities are other unresolved challenges.
SUMMARY
[004] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method for Strength Weakness Opportunity and Threat (SWOT) analysis of a research area from unstructured data using technology landscaping is provided. The method includes receiving a global topic set, generated by a topic model by processing global documents from a global corpus generated for a predefined time span. The global topic set comprises a predefined number of topics derived from global documents comprising unstructured data, wherein each topic among the global topic set is assigned a topic score per global document to generate document-to-topics pairing for each of the global documents. Further receives an enterprise topic set comprising one or more topics from among the global topic set, generated by the topic model, by processing enterprise documents comprising the unstructured data from an enterprise corpus generated for the predefined time span and belonging to a plurality of research areas of the enterprise. Each topic among the enterprise topic set is assigned the topic score per enterprise document to generate the document-to-topics pairing for each of the enterprise documents with the topics from the enterprise topic. Furthermore, receives a research area of interest among the plurality of research areas for performing the SWOT analysis;
[005] Further, the method comprises mapping a plurality of topics identified from among the enterprise topic set and the global topic set to the research area of interest, wherein mapping is based on the topic score and an iterative topic allocation process. Further, the method comprises computing a topic trend score for the plurality of topics mapped to the research area, wherein topic

trend score is derived from percentage growth or decline in a topic frequency of the topic over per year over a period of time, wherein the trend score comprises a) an enterprise trend score computed based on occurrence of the topic in publications present in the enterprise corpus, and b) a global trend score computed based on occurrence of the topic in publications present in the global corpus. Furthermore, the method comprises filtering the plurality of topics, based a topic relevance score for each of the plurality of topics to identify a set of significant topics in the research area of interest, wherein the set of significant topics have the topic relevance score above a predefined topic relevance score threshold. The topic relevance score of each of the plurality of topics of the research area is obtained by: ranking each of the plurality of topics in a given year based on the topic trend score of each topic; and computing a mean ranking adding for each of the plurality of topics by adding a decaying weightage factor to the ranking of previous years of each of the plurality of topics, wherein the mean ranking provides the topic relevance score for each of the plurality of topics.
[006] Further, the method comprises calculating a percentage topic intensity of each of the plurality of topics based on ratio of the topic frequency of a topic to sum of topic frequencies of the set of significant topics. Further, the method comprises calculating, a topic momentum for each of the set of significant topics by taking product of the percentage topic intensity and the enterprise topic trend score associated with each of the set of significant topics. Furthermore, the method comprises computing a correlation between growing trend of each of the set of significant topics at a global level with growing trend of corresponding each of the set of significant topics within the enterprise by computing a Pearson Correlation Coefficient (PCC) for each of the topic based on the enterprise trend score and the global trend score for the topic. Thereafter, the method comprises creating (a) SW quadrants providing Strengths (S) and Weaknesses (W) and (b) OT quadrants providing Opportunities (O), and Threats (T) the SWOT analysis of the set of significant topics, wherein the SW quadrants comprise the topic momentum plotted on Y-axis and the PCC plotted on X-axis, wherein topics in an upper right quadrant represent the Strengths (S) of the enterprise and topics in a

lower right quadrant represent the Weaknesses (W) of the enterprise, and wherein a strength measure and a weakness measure is calculated by determining distance of the topic from origin of the SW quadrants with sign of the measure derived from the momentum indicating a strength or a weakness.
[007] In another aspect, a system for Strength Weakness Opportunity and Threat (SWOT) analysis of a research area from unstructured data using technology landscaping.is provided. The system comprises a memory storing instructions; one or more Input/Output (I/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to receive a global topic set, generated by a topic model by processing global documents from a global corpus generated for a predefined time span. The global topic set comprises a predefined number of topics derived from global documents comprising unstructured data, wherein each topic among the global topic set is assigned a topic score per global document to generate document-to-topics pairing for each of the global documents. Further receive an enterprise topic set comprising one or more topics from among the global topic set, generated by the topic model, by processing enterprise documents comprising the unstructured data from an enterprise corpus generated for the predefined time span and belonging to a plurality of research areas of the enterprise. Each topic among the enterprise topic set is assigned the topic score per enterprise document to generate the document-to-topics pairing for each of the enterprise documents with the topics from the enterprise topic. Furthermore, receive a research area of interest among the plurality of research areas for performing the SWOT analysis;
[008] Further, the one or more hardware processors are configured to map a plurality of topics identified from among the enterprise topic set and the global topic set to the research area of interest, wherein mapping is based on the topic score and an iterative topic allocation process. Further, the one or more hardware processors are configured to compute a topic trend score for the plurality of topics mapped to the research area, wherein topic trend score is derived from percentage growth or decline in a topic frequency of the topic over per year over a period of

time, wherein the trend score comprises a) an enterprise trend score computed based on occurrence of the topic in publications present in the enterprise corpus, and b) a global trend score computed based on occurrence of the topic in publications present in the global corpus. Furthermore, the one or more hardware processors are configured to filter the plurality of topics, based a topic relevance score for each of the plurality of topics to identify a set of significant topics in the research area of interest, wherein the set of significant topics have the topic relevance score above a predefined topic relevance score threshold. The topic relevance score of each of the plurality of topics of the research area is obtained by: ranking each of the plurality of topics in a given year based on the topic trend score of each topic; and computing a mean ranking adding for each of the plurality of topics by adding a decaying weightage factor to the ranking of previous years of each of the plurality of topics, wherein the mean ranking provides the topic relevance score for each of the plurality of topics.
[009] Further, the one or more hardware processors are configured to calculate a percentage topic intensity of each of the plurality of topics based on ratio of the topic frequency of a topic to sum of topic frequencies of the set of significant topics. Further, the one or more hardware processors are configured to calculate, a topic momentum for each of the set of significant topics by taking product of the percentage topic intensity and the enterprise topic trend score associated with each of the set of significant topics. Furthermore, the one or more hardware processors are configured to compute a correlation between growing trend of each of the set of significant topics at a global level with growing trend of corresponding each of the set of significant topics within the enterprise by computing a Pearson Correlation Coefficient (PCC) for each of the topic based on the enterprise trend score and the global trend score for the topic. Thereafter, the one or more hardware processors are configured to create (a) SW quadrants providing Strengths (S) and Weaknesses (W) and (b) OT quadrants providing Opportunities (O), and Threats (T) the SWOT analysis of the set of significant topics, wherein the SW quadrants comprise the topic momentum plotted on Y-axis and the PCC plotted on X-axis, wherein topics in an upper right quadrant represent

the Strengths (S) of the enterprise and topics in a lower right quadrant represent the Weaknesses (W) of the enterprise, and wherein a strength measure and a weakness measure is calculated by determining distance of the topic from origin of the SW quadrants with sign of the measure derived from the momentum indicating a strength or a weakness.
[0010] In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions, which when executed by one or more hardware processors causes a method for Strength Weakness Opportunity and Threat (SWOT) analysis of a research area from unstructured data using technology landscaping..
[0011] The method includes receiving a global topic set, generated by a topic model by processing global documents from a global corpus generated for a predefined time span. The global topic set comprises a predefined number of topics derived from global documents comprising unstructured data, wherein each topic among the global topic set is assigned a topic score per global document to generate document-to-topics pairing for each of the global documents. Further receives an enterprise topic set comprising one or more topics from among the global topic set, generated by the topic model, by processing enterprise documents comprising the unstructured data from an enterprise corpus generated for the predefined time span and belonging to a plurality of research areas of the enterprise. Each topic among the enterprise topic set is assigned the topic score per enterprise document to generate the document-to-topics pairing for each of the enterprise documents with the topics from the enterprise topic. Furthermore, receives a research area of interest among the plurality of research areas for performing the SWOT analysis;
[0012] Further, the method comprises mapping a plurality of topics identified from among the enterprise topic set and the global topic set to the research area of interest, wherein mapping is based on the topic score and an iterative topic allocation process. Further, the method comprises computing a topic trend score for the plurality of topics mapped to the research area, wherein topic trend score is derived from percentage growth or decline in a topic frequency of the topic over per year over a period of time, wherein the trend score comprises a)

an enterprise trend score computed based on occurrence of the topic in publications present in the enterprise corpus, and b) a global trend score computed based on occurrence of the topic in publications present in the global corpus. Furthermore, the method comprises filtering the plurality of topics, based a topic relevance score for each of the plurality of topics to identify a set of significant topics in the research area of interest, wherein the set of significant topics have the topic relevance score above a predefined topic relevance score threshold. The topic relevance score of each of the plurality of topics of the research area is obtained by: ranking each of the plurality of topics in a given year based on the topic trend score of each topic; and computing a mean ranking adding for each of the plurality of topics by adding a decaying weightage factor to the ranking of previous years of each of the plurality of topics, wherein the mean ranking provides the topic relevance score for each of the plurality of topics.
[0013] Further, the method comprises calculating a percentage topic intensity of each of the plurality of topics based on ratio of the topic frequency of a topic to sum of topic frequencies of the set of significant topics. Further, the method comprises calculating, a topic momentum for each of the set of significant topics by taking product of the percentage topic intensity and the enterprise topic trend score associated with each of the set of significant topics. Furthermore, the method comprises computing a correlation between growing trend of each of the set of significant topics at a global level with growing trend of corresponding each of the set of significant topics within the enterprise by computing a Pearson Correlation Coefficient (PCC) for each of the topic based on the enterprise trend score and the global trend score for the topic. Thereafter, the method comprises creating (a) SW quadrants providing Strengths (S) and Weaknesses (W) and (b) OT quadrants providing Opportunities (O), and Threats (T) the SWOT analysis of the set of significant topics, wherein the SW quadrants comprise the topic momentum plotted on Y-axis and the PCC plotted on X-axis, wherein topics in an upper right quadrant represent the Strengths (S) of the enterprise and topics in a lower right quadrant represent the Weaknesses (W) of the enterprise, and wherein a strength measure and a weakness measure is calculated by determining distance

of the topic from origin of the SW quadrants with sign of the measure derived from the momentum indicating a strength or a weakness.
[0014] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[0016] FIG. 1A is a functional block diagram of a system for Strength Weakness Opportunity and Threat (SWOT) analysis of a research area from unstructured data using technology landscaping., in accordance with some embodiments of the present disclosure.
[0017] FIG. 1B illustrates an architectural and process overview of the system of FIG. 1, in accordance with some embodiments of the present disclosure.
[0018] FIG. 2A and 2B (collectively referred as FIG. 2) is a flow diagram illustrating a method for Strength Weakness Opportunity and Threat (SWOT) analysis of the research area from unstructured data using technology landscaping., using the system of FIG. 1, in accordance with some embodiments of the present disclosure.
[0019] FIGS. 3A and 3B (collectively referred as FIG. 3) are example Strength Weakness (SW) quadrants and Opportunity Threat (OT) quadrants generated for significant set of topics from among topics mapped to the research area of interest of an enterprise, in accordance with some embodiments of the present disclosure.
[0020] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems and devices embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various

processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION OF EMBODIMENTS [0021] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.
[0022] Conventionally, Strength Weakness Opportunity and Threat (SWOT) analysis is referred as a strategic planning technique used to help a person or organization identify strengths, weaknesses, opportunities, and threats related to business competition or project planning. Most of the existing works generating forecasts for an enterprise or organization follow the conventional definition of SWOT analysis and provide qualitative insights that rely on manual interventions. [0023] Embodiments of the present disclosure provide a method and system for Strength Weakness Opportunity and Threat (SWOT) analysis of a research area from unstructured data using technology landscaping.. The method disclosed defines the SWOT analysis as analysis of an enterprise’s research capability within a given scope of the research area or sub area and further identifying which topics within the given scope can be considered as strengths, weaknesses, opportunities, and threats. The method provides the enterprise and competitor aware integrated SWOT analysis with quantitative analysis, thus giving a 360-degree insights on future road map of the enterprise over the research area of interest. The quantitative measure provides values of each of the S, W, O and T factors critical to enterprise forecasts, which are in turn derived from measurable parameters defined by the system such as topic relevance score, topic trend score, topic intensity, topic momentum, topic correlation, industry fitment score per topic,

that enable to group technologies generating the technology landscape for the SWOT analysis. These values are then used to position the topics into Strength Weakness (SW) quadrants and Opportunity Threat (OT) quadrants with coordinates of the topics defined by topic momentum and the correlation coefficient for the SW quadrants, and a strength measure obtained from SW quadrants and an industry fitment score for the OT quadrants. Further, SW and OT quadrants are also generated for one or more competitors using accessible competitors corpus to derive comparative insights on whether a topic among the topics of the research areas of the enterprise is an opportunity for the enterprise or a competitor is a threat for the topic with severe competition.
[0024] Referring now to the drawings, and more particularly to FIGS. 1A through 3B, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[0025] FIG. 1A is a functional block diagram of a system for Strength Weakness Opportunity and Threat (SWOT) analysis of the research area from unstructured data using technology landscaping, in accordance with some embodiments of the present disclosure.
[0026] In an embodiment, the system 100 includes a processor(s) 104, communication interface device(s), alternatively referred as input/output (I/O) interface(s) 106, and one or more data storage devices or a memory 102 operatively coupled to the processor(s) 104. The system 100 with one or more hardware processors is configured to execute functions of one or more functional blocks of the system 100.
[0027] Referring to the components of system 100, in an embodiment, the processor(s) 104, can be one or more hardware processors 104. In an embodiment, the one or more hardware processors 104 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the

one or more hardware processors 104 are configured to fetch and execute computer-readable instructions stored in the memory 102. In an embodiment, the system 100 can be implemented in a variety of computing systems including laptop computers, notebooks, hand-held devices such as mobile phones, workstations, mainframe computers, servers, and the like.
[0028] The I/O interface(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface to display the generated target images and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular and the like. In an embodiment, the I/O interface (s) 106 can include one or more ports for connecting to a number of external devices or to another server or devices.
[0029] The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
[0030] FIG. 1B illustrates an architectural and process overview of the system 100 of FIG. 1, in accordance with some embodiments of the present disclosure. This, the memory 102 includes a database 108 that stores a global topic set derived from global corpus, an enterprise topic set derived from enterprise corpus, enterprise relevant topics from among the plurality of topics to be taken forward for SWOT analysis (referred as significant topic set), a competitors topic set derived from competitor’s corpus, an industry topic set derived from industry corpus related to a plurality of industries of interest to the enterprise, as depicted in FIG. 1B. Further, the memory 102 may comprise information pertaining to input(s)/output(s) of each step performed by the processor(s) 104 of the system100 and methods of the present disclosure. As depicted in FIG. 1B, the memory includes a plurality of topics mapped to research area of interest of the enterprise, a topic relevance score of the plurality of topics, and a topic trend score, a topic intensity,

a topic momentum, a topic correlation (Pearson Correlation Coefficient(PCC)), an industry fitment score computed for a set of significant topics filtered from the plurality of topics, and the like. In an embodiment, the database 108 may be external (not shown) to the system 100 and coupled to the system via the I/O interface 106.
[0031] The global corpus, the enterprise corpus, the industry corpus, and the competitor’s corpus for one or more competitors is generated by the system by crawling data from all available external and internal resources accessed via the I/O interface 106. Further the global topic set, the enterprise topic set, industry topic set, the competitor’s topic set are generated using a topic model in accordance with technology disclosed by the Applicant in the Indian Patent Application No. 202121035610. However, it may be understood by those skilled in the art that the topic sets of interest to the enterprise may be obtained by methods provided in the Applicant’s Patent Application No. 202121035610 or by any other methods known in the art. Similarly, the topics from the global topic set and the enterprise topic set that are mapped to an research area of interest of the enterprise is also obtained using the technology disclosed in Applicant in the Indian Patent Application No. 202121035610 and not repeated herein for brevity.
[0032] Once the topics sets of interest and the plurality of topics for the research area of interest are identified, the system 100 proceeds with SWOT analysis using the components of the system 100, which are explained in conjunction with flow diagram of FIG. 2 and the SW quadrants and OT quadrants of FIG. 3.
[0033] FIG. 2A and 2B (collectively referred as FIG. 2) is a flow diagram illustrating a method for Strength Weakness Opportunity and Threat (SWOT) analysis of the research area from unstructured data using technology landscaping., using the system of FIG. 1, in accordance with some embodiments of the present disclosure.
[0034] In an embodiment, the system 100 comprises one or more data storage devices or the memory 102 operatively coupled to the processor(s) 104 and is configured to store instructions for execution of steps of the method 200 by the processor(s) or one or more hardware processors 104. The steps of the method 200

of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG. 1A, 1B and the steps of flow diagram as depicted in FIG. 2. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods, and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps to be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.
[0035] Referring to the steps of the method 200, at step 202 of the method 200, the one or more hardware processors 104 receives the global topic set, generated by a topic model by processing global documents from a global corpus generated for a predefined time span. The global topic set comprises a predefined number of topics derived from global documents comprising unstructured data such as whitepapers, enterprise reports, patent literature, and the like. Each topic among the global topic set is assigned a topic score per global document to generate document-to-topics pairing for each of the global documents. Only the topics from the global corpus that satisfy a predefined topic score threshold are selected into the global topic set. For example: the criteria can be ‘assign topics to document if topic score for the document is above 0.2 (wherein doc-topic score lies between 0 and 1)
[0036] Also, received is then enterprise topic set comprising one or more topics from among the global topic set, generated by the topic model, by processing enterprise documents comprising unstructured documents from an enterprise corpus generated for the predefined time span and belonging to a plurality of research areas of the enterprise. The topics from the enterprise corpus having the topic scores above the predefined threshold are identified in the enterprise topic set. Each topic among the enterprise topic set is assigned the topic score per enterprise document to generate the document-to-topics pairing for each of the enterprise documents with the topics from the enterprise topic. Furthermore, to initiate SWOT analysis, also received is the research area of interest from among a plurality of research areas of the enterprise. It is to be understood that term

research and research sub area can be used interchangeably. Further, based on the enterprise requirement, the SWOT analysis may be carried out for a research are or a research sub area.
[0037] Once the global topic set and the enterprise topic set is generated, then for a received research area of interest, at step 204, the one or more hardware processors 104 map a plurality of topics identified from among the enterprise topic set and the global topic set to the research area of interest. The mapping is based on the topic score and an iterative topic allocation process.
[0038] Once the plurality of topic sets mapped to the research area of interest are identified, at step 206, of the method 200, the one or more hardware processors 104 compute a topic trend score for the plurality of topics mapped to the research area. The topic trend score is derived from percentage growth or decline in a topic frequency of the topic per year over a period of time. The trend score comprises a) an enterprise trend score computed based on occurrence of the topic in publications present in the enterprise corpus, and b) a global trend score computed based on occurrence of the topic in publications present in the global corpus. The topic trend score is computed, as in Applicant’s in the Indian Patent Application No. 202121035610 and is stated below:
a) selecting documents published in a given year
b) counting the frequency of documents for each topic
c) computing the average volume of documents per year over a time range.
d) Normalizing the topic frequencies to the average volume
e) Plotting the topic frequencies over the years in time range.
f) Identifying topic trend as percentage of growth over the last year (+ or -)
g) Rank topics in each year according to topic counts across the corpus.
[0039] At step 208, of the method 200, the one or more hardware processors
104 filter the plurality of topics, based a topic relevance score computed for each of the plurality of topics. The topic relevance score is derived from the topic relevance score and ranking obtained in step 206 and enables identifying the set of significant topics from among the plurality of topic, for which SWOT analysis

should be initiated. Thus, this step eliminates unnecessary computation of less relevant topic across the plurality of topics mapped to the research area, saving computation and memory resources. It can be understood that within the research area the enterprise may have been more focused on certain topics as they are critical topics, while less focused on other. Thus, method enables first determining the trend within the enterprise for the research area of interest and accordingly using the trend score to eliminate least relevant topics. The filtering generates a set of significant topics in the research area of interest having the topic relevance score above a predefined topic relevance score threshold, which can be set by the enterprise or subject matter expert based on the requirement. The topic relevance score of each of the plurality of topics of the research area is obtained by ranking each of the plurality of topics in a given year based on the topic trending score. Further, a mean ranking adding is computed for each of the plurality of topics by adding a decaying weightage factor ( 1 / decay factor)number of years) to the ranking of previous years of each of the plurality of topics, wherein the mean ranking provides the topic relevance score each of the plurality of topics.
[0040] At step 210, of the method 200, the one or more hardware processors 104 calculate a percentage topic intensity of each of the plurality of topics based on ratio of the topic frequency of a topic to sum of topic frequencies of the set of significant topics. Thus, % topic intensity = (topic frequency of given topic / topic frequencies of all topics) * 100. Thereafter, at step 212, of the method 200, the one or more hardware processors 104 calculate a topic momentum for each of the set of significant topics by taking product of the percentage topic Intensity and the enterprise topic trend score associated with each of the set of significant topics. At step 214, of the method 200, the one or more hardware processors 104 compute, by the one or more hardware processors, a correlation between growing trend of each of the set of significant topics at a global level with growing trend of corresponding each of the set of significant topics within the enterprise by computing the Pearson Correlation Coefficient (PCC) for each of the topic based on the enterprise trend score and the global trend score for the topic. Thus, obtained

is a correlation between growth of each topic within the research area of interest at a global level with the growth of the same topic within the enterprise.
[0041] At step 216, of the method 200, the one or more hardware processors 104 create (a) SW quadrants providing Strengths (S) and Weaknesses (W), and (b) OT quadrants providing Opportunities (O) and Threats (T) the SWOT analysis of the set of significant topics (216). The SW quadrants comprise the topic momentum plotted on Y-axis and the PCC plotted on X-axis, wherein topics in an upper right quadrant represent the Strengths (S) of the enterprise and topics in a lower right quadrant represent the Weaknesses (W) of the enterprise. A measure of a strength and a weakness (also referred as strength measure and weakness measure)is calculated by determining distance of the topic from the origin of the SW quadrants with sign of the measure derived from the momentum indicating a strength or a weakness. Computation of the strength measure and the weakness measure is depicted in the illustrative example stated later. FIGS. 3A and 3B (collectively referred as FIG. 3) are example SW quadrants and OT quadrants generated for significant set of topics from among topics mapped to the research area of interest of an enterprise, in accordance with some embodiments of the present disclosure. FIG. 3 is explained in conjunction with illustrative example provided below. On the SW quadrants generated for the set of significant topics, the topic momentum capture two dimensions of a topic. The topic intensity (i.e. amount of document published for given topic in given year range- this captures the velocity of topic globally) and topic trend (trend score captures the net topic growth/decline within the enterprise). So, the topic momentum captures the combined effect of intensity and growth in the year range. The topic momentum is negative when the net topic trend for enterprise is negative. The correlation parameter (for example the PCC) is used to determine if the topic growth for the research area of interest in enterprise is in line with the global topic growth for the research area of interest. So, a positive correlation shows enterprise topic growth is aligned with global, i.e. if global topic trend is decreasing, enterprise topic trend also shows decrease. Thus, the method disclosed herein considers only the positive correlation to determine S/W. For a topic to be called as Strength, the topic should

have positive momentum and high correlation indicating that the enterprise is aligned for a growing topic. The topic becomes weakness when the enterprise shows a decline trend for topic. From the position in quadrant, one can determine the strength / weakness measure for topic by calculating distance from origin as depicted in the illustrative example below. The computation of the strength measure and the weakness measure is depicted in the illustrative example stated later. This value is used to find if the topic identified as strength can translate to opportunity, and check if a topic identified as weakness can be a possible threat for the enterprise.
[0042] To generate opportunity and threat analysis the method 200 focuses on industries of interest to the enterprise and analyzes the current trending topics across the industries of interest and further, in the context of the industry trending topics, performs the OT analysis. Even though, reference above is to industries of interest, similar OT analysis can be performed for industry segments of interest within an industry to identify opportunities and threats for the research topics across the industry segments. Thus, the method provides OT analysis across different industry levels.
[0043] The OT quadrant is generated based on an industry fitment score. Creating the OT quadrants comprise steps of :
a) Receiving industry documents, comprising unstructured data, for a plurality of industries of interest from industry-tracking websites and analyst reports. This generates the industry corpus wherein the industry documents are collected for the predefined time span.
b) Creating an industry topic set for each industry among a plurality of industries of interest, with each topic among the industry topic set having the topic score per industry document for each of the plurality of industries of interest. As mentioned earlier the topic score is generated by the topic model by processing the industry documents corresponding to respective industry among the plurality of industries of interest and topics having the topic scores above the predefined threshold are identified in the industry topic set.

c) Filtering, from the industry topic set, topics that map to the research area of interest. This step enables to focus on only those topics in the industry that are relevant to the research area of interest of the enterprise.
d) Aggregating the topic score of each topic from among the filtered topics across the plurality the industry documents for each of the plurality of industries of interest.
e) Computing an industry fitment score for topics for each industry by dividing the aggregated topic scores of each topic by a total number of industry documents for the industry and a constant scale factor derived empirically, for example, the constant scale factor can be selected between scale of within 1 to 10. The constant scale factor value enables to set the readability of the quadrants.
f) Computing an overall industry fitment score for the topic among the filtered topics by aggregating the industry fitment score across the plurality of industries of interest. The industry fitment score indicates how much application a topic (from among the industry topic set obtained by inferring industry documents via the topic model) has in industries of interest. The enterprise can choose to work on topics which are of pure research interest or topics which are used to solve problems of industry. The industry fitment score calculates how relevant the topic is for the industry of interest. For example, a topic like ‘private key encryption’ can be used in following industries: blockchain, banking, finance , networking , supply chain while a topic like gene editing has specific application in personalized medicine industry.
g) Creating the OT quadrants for the SWOT analysis, with the Opportunities (O) represented by an upper right quadrant and the Threats (T) represented by a lower right quadrant, wherein the topics among the industry mapping topics are plotted as an opportunity or a threat based on industry fitment scores plotted on the X-axis and the Strengths and Weaknesses, obtained from SW quadrants, on the Y-axis.

The topic is strength for enterprise if it lies in (S) quadrant of SW
quadrants (SW chart). The strength topic becomes an opportunity if the
topic has good industry fitment score and less competitors. A weak
topic for enterprise becomes a threat if the topic is present as strength
for many competitors.
[0044] The industry fitment score, disclosed herein enables to determine
how many industries of interest for enterprise have use of the topic (s) from among
the set of significant topics identified for the enterprise, providing a business view
of topic. Thus, if the topic is used in many industries it is provided a high industry
fitment score. Referring to the set of significant topics identified as strength and
weakness in the SW quadrants, generating the OT quadrants (OT charts) from the
topic positions of the set of significant topics enables deriving an opportunity score
(also referred as opportunity measure) or threat score (also referred as threat
measure) for the enterprise. Computation of the opportunity measure and the threat
measure is depicted in the illustrative example stated below. The enterprise, based
on expertise, can set the threshold of the opportunity measure above which it
defines a topic as opportunity. A high opportunity score implies more chances of
becoming opportunity. A weak topic for enterprise has chances of becoming a
threat if the same topic is present as Strength in competitors. Same procedure is
used to determine the threat score as opportunity score. The difference lies in
interpretation. A low score implies more chances of being a threat since the value
is negative.
[0045] The opportunity for the topic is measured in terms of an opportunity measure, as distance of the topic lying in the upper right of the OT quadrants from the origin divided by number of competitors having the same topic as strength. Similarly, the threat for the topic is measured in terms of the threat measure as distance of the topic lying in the lower right of the OT quadrants from the origin divided by number of competitors having the same topic as strength.
[0046] The method further comprises creating the SW quadrants and the OT quadrants for one or more competitors of interest by analyzing unstructured corpus obtained for the one or more competitors. The OT quadrants of the

enterprise are compared with the OT quadrants for one or more competitors to derive comparative insights. The OT quadrants comparison directly provides information on whether a topic, among the significant topics, is an opportunity for the enterprise; or a competitor is a threat for the enterprise for the topic. If a topic is strength of the enterprise, but there are many competitors present (which is determined by OT analysis or one or more competitors of interest), then the topic will not be an opportunity. Similar comparison for threat is followed. If a topic is weakness and there is present just one competitor, then it does not become a threat for enterprise.
[0047] ILLUSTRATIVE EXAMPLE: Various score computations for topics T1 through T5 identified for the research area of interest:
A. Topic trend(Global) score:
1. Suppose topic t1 has publication count as pub1Year1 in year1 ; Total doc in year1 is docYear1
2. Suppose topic t1 has publication count as pub2Year2 in year2 ; Total doc in year2 is docYear2
3. Suppose topic t1 has publication count as pub2Year3 in year3 ; Total doc in year3 is docYear3
4. Normalizing: NT1Y1 = pub1Year1 /docYear1; NT1Y2 = pub2Year2 /docYear2; NT1Y3 = pub3Year3/docYear3;
5. Growth: G1Y2Y1T1=(NT1Y2-NT1Y1)/NT1Y1 * 100 ; G1Y3Y2T1=(NT1Y3 - NT1Y2)/NT1Y3 * 100;
6. Trend score for topic1- TrendScoreT1 = (G1Y2Y1T1 + G1Y3Y2T1)/2
7. Similar topic trend score can be calculated for topics T2 , T3 , T4,T5 AS TrendScoreT2, TrendScoreT3, TrendScoreT4, TrendScoreT5
8. Mean to topic trend score is given by MeanTopicTrendGlobalScore =( TrendScoreT1 + TrendScoreT2+ TrendScoreT3+ TrendScoreT4+ TrendScoreT1)/5
B. Topic trend score within enterprise E1:
1. Suppose topic t1 has publication count as pub1E1Year1 in year1 ; Total doc in year1 is docE1Year1

2. Suppose topic t1 has publication count as pub2E1Year2 in year2 ; Total doc in year2 is docE1Year2
3. Suppose topic t1 has publication count as pub2E1Year3 in year3 ; Total doc in year3 is docE1Year3
4. Normalizing: NT1E1Y1 = pub1E1Year1 /docE1Year1; NT1E1Y2 = pub2E1Year2 /docE1Year2; NT1E1Y3 = pub3E1Year3/docE1Year3;
5. Growth: G1E1Y2Y1T1=(NT1E1Y2-NT1E1Y1)/NT1E1Y1 ; G1E1Y3Y2T1=(NT1E1Y3 - NT1E1Y2)/NT1E1Y3
6. Trend score for topic t1 , TrendScoreT1E1 = (G1Y2Y1E1T1 + G1Y3Y2E1T1)/2
7. Similar topic trend score can be calculated for topics T2, T4, T5 TrendScoreT2E1, TrendScoreT2E1, TrendScoreT2E1
8. Mean to topic trend score is given by MeanTopicTrendE1Score = (TrendScoreT1E1+ TrendScoreT2E1+ TrendScoreT4E1+ TrendScoreT5E1)/4
9. Similar step can be followed to get the trend score for another enterprise/competitor
C. Computing topic relevance score of each topic within the research area/research sub-area:
1. Suppose Research area RA1 of Enterprise E1, has topic T1,T5,T9 ranked by their enterprise trend score (TrendScoreTN);
2. Topic1 in enterprise has rank T1R1 in Year1 , T1R2 in Year2 , T1R3 in Year3
3. and Year2=Year1+1; Year3=Year2+1;
4. So mean rank for topic MRE1T1 is given by(choosing decay factor as DF):
5. MRE1T1 = [T1R1*(1/DF)^2 + T1R2*(1/DF)^1 + T1R3*(1/DF)^0]/3
6. Topic5 in enterprise E1 has rank T5R2 in Year2 , T5R3 in Year3:
7. MRE1T5 = [T5R1*(1/DF)^1 + T5R2*(1/DF)^0]/2

D. For a research area and an enterprise: Topic Momentum value for each topic by multiplying the Topic Intensity by the Topic Trend:
1. Suppose topic T1 has total publication between year y1 to y3
docT1E1Y1Y3;
2. Suppose topic T2 has total publication between year y1 to y3 docT2E1Y1Y3;
3. Suppose topic T3 has total publication between year y1 to y3 docT3E1Y1Y3;
4. Suppose topic T4 has total publication between year y1 to y3 docT4E1Y1Y3;
5. Topic intensity is calculated as:
Topic intensity for topic T1 is intensityE1T1 =
docT1E1Y1Y3/(docT1E1Y1Y3 + docT2E1Y1Y3 + docT3E1Y1Y3
+docT4E1Y1Y3) * 100;
Topic intensity for topic T2 is intensityE1T2 =
docT2E1Y1Y3/(docT1E1Y1Y3 + docT2E1Y1Y3 + docT3E1Y1Y3
+docT4E1Y1Y3) * 100;
Topic intensity for topic T3 is intensityE1T3 =
docT3E1Y1Y3/(docT1E1Y1Y3 + docT2E1Y1Y3 + docT3E1Y1Y3
+docT4E1Y1Y3) * 100;
Topic intensity for topic T4 is intensityE1T4 =
docT4E1Y1Y3/(docT1E1Y1Y3 + docT2E1Y1Y3 + docT3E1Y1Y3
+docT4E1Y1Y3) * 100;
6. Momentum for topic is given by (multiply by 1/100 to scale value
between 0 to 10):
Momentum for topic T1 is = intensityE1T1 * TrendScoreT1E1 *
1/100
Momentum for topic T2 is t2E1Moementum2 = intensityE1T2 *
TrendScoreT2E1 * 1/100

Momentum for topic T3 is t3E1Moementum3 = intensityE1T3 * TrendScoreT3E1 * 1/100
7. Topic Correlation for enterprise E1:
Topic correlation for each topic T1, topicCorrelationE1T1 = [ ( TrendScoreT1E1 - MeanTopicTrendE1Score)(TrendScoreT1 -MeanTopicTrendGlobalScore) + ( TrendScoreT2E1 -MeanTopicTrendE1Score)(TrendScoreT2 -MeanTopicTrendGlobalScore)
+ ( 0 ) (TrendScoreT3 - MeanTopicTrendGlobalScore)+ (
TrendScoreT4E1 - MeanTopicTrendE1Score)(TrendScoreT4 -
MeanTopicTrendGlobalScore) + ( TrendScoreT5E1 -
MeanTopicTrendE1Score)(TrendScoreT5 -MeanTopicTrendGlobalScore) ] ÷ [ ( TrendScoreT1E1 -MeanTopicTrendE1Score)(TrendScoreT1 -MeanTopicTrendGlobalScore)
+ ( TrendScoreT2E1 - MeanTopicTrendE1Score)(TrendScoreT2 -MeanTopicTrendGlobalScore) + ( 0 ) (TrendScoreT3 -MeanTopicTrendGlobalScore) + ( TrendScoreT4E1 -MeanTopicTrendE1Score)(TrendScoreT4 -MeanTopicTrendGlobalScore) + ( TrendScoreT5E1 -MeanTopicTrendE1Score)(TrendScoreT5 -MeanTopicTrendGlobalScore) ]^0.5
8. Strength: Strength measure for topic T1 strengthE1T1= (topicCorrelationE1T1^2 + t1E1Moementum1^2)^0.5
9. Weakness: Weakness measure for topic T3 weaknessE1T3= (topicCorrelationE1T3^2 + t3E1Moementum1^2)^0.5
10. Similar steps above are followed for other enterprises (competitors) E2, E3
E. Industry topic fitment:
1. Suppose for industry ID1, docs are D1, D2, D3 till Dn
2. Inferred topics document for D1: T1=doc1T1Score1; T3=doc1T3Score3;

3. Inferred topics document for D2: T1=doc2T2Score1; T2=doc2T2Score2; T3=doc2T3Score3;
4. Inferred topics document for D1: T2=doc3T2Score2; T3=doc3T3Score3;
5. Aggregated topic score AGID1T1 = (doc1T1Score1 + doc2T2Score1)/n
6. Aggregated topic score AGID1T3 = (doc1T3Score3+ doc2T3Score3+ doc3T3Score3)/n
7. Fitment score for industry I1, topic T1 is fitmentScore1T1 = (AGID1T1 + AGID2T1 + AGID3T1 + AGID4T1)/4
F. Opportunity-Threat
1. Opportunity: (No. of competitors is 1)
2. Opportunity measure = [ (fitmentScore1T1^2 + strengthE1T1^2)^0.5]/1
3. Threat: (No. of competitors is 2)
4. Threat measure = [ (fitmentScore1T1^2 + weaknessE1T3^2)^0.5]/2 [0048] As depicted in FIG. 3A and 3B it can be understood that for the SW
quadrants, a topic to further right and further top, signifies it is a strength. This is because the topic has got good momentum and its correlation score (PCC) is high (correlation signifies the topic growth is in line with global topic growth). Similarly , if the topic has negative momentum , it becomes a weakness for the enterprise. So, if an enterprise is working on such topic it is a weakness for it. In case of OT quadrant, industry fitment score for topic is on x axis. So, a topic which is very aligned with industry is on further right of x axis. The topic strength/weakness measure from SW analysis is plotted on y axis. For a topic to be an opportunity, topic should be in further right and top. Also, number of competitors should be less since we get the strength measure by dividing the distance with number of competitors with strength. However, a topic is threat, if there exist more competitors (who have the same topic as strength). Less score implies more threat since the sign of the treat measure or threat score is negative.
[0049] The SWOT analysis generated by the method disclosed, provides direct insights for future roadmaps to the enterprise. In addition, the SWOT analysis output generated by the system 100 can be consumed by other tools and platform for further analysis and insights. This enables an automated input data

generation for these tools and platforms. Research and development (R&D) is the
most popular means by which corporations and other institutions accumulate and
increase the knowledge pool. This knowledge pool provides vital inputs to the
company to grow and diversify its products and services, often in response to
external factors as described by Porter’s Five Forces Model. To maintain their
current position and grow, enterprises employ tools that help track the 5 forces and
devise appropriate steps. Lucidity™ (https://getlucidity.com/strategy-
resources/create-porters-five-forces-in-6-steps/) is one widely used tool which is popular. There are other tools that employ Clayton Christiansen’s Quadrant Model popularly known as Clay Maps™ (https://bhc3.com/2009/12/01/the-four-quadrants-of-innovation-disruptive-vs-incremental/). There are tool developed to build and analyze Clay Maps. These tools can use the SWOT analysis disclosed herein as a plug-in application. The SWOT Analysis application is able to serve these tools by providing crucial inputs. As an example, the Lucidity™ tool is picked as an example to depict how it consumes the SWOT analysis:
[0050] Competition in the Industry: This is one of the five forces of Porter’s Model™ that account for a company’s sustainability. The SWOT analysis provides a direct measure of competition by identifying the number of key competitors that have trending topics in the research area/ sub area related to the industry segment as strengths.
[0051] Potential of New Entrants into an Industry: This another of the five forces that unearth further risks to company’s (enterprise’s) growth and sustainability. The SWOT analysis is able to identify companies which are seeing the trending topics in the relevant industry segment as opportunities to exploit. These companies represent the potential new entrants into the industry.
[0052] Threat of Substitutes: The topics that represent the weaknesses of the company or enterprise have the potential to become substitutes for the products and services offered by the company. Hence a measure of this force can also be provided by the SWOT Analysis tool for a strategy tool to help analyze and formulate appropriate actions.

[0053] Clayton Christiansen’s Clay Diagrams Model™ is used to categorize the growth strategy of the company. The model classifies growth as incremental, break-through, disruptive and game changing. Companies benefit from all four strategies but involve different risks and rewards. The SWOT Analysis helps identify topics for each of the strategy which are key inputs to the Clay Mao tool. The following describes how the SWOT Analysis application provides inputs to the tool:
[0054] Incremental Growth Strategy: Incremental innovations involve modest changes to existing products and services. These are enhancements that keep a business competitive. The SWOT Analysis helps identify the topics that need to be considered in a sub area to pursue an incremental growth strategy.
[0055] Break-Through Strategy: It refers to large technological advances that propel an existing product or service ahead of competitors. This is often the result of research and development labs focusing on emerging opportunity areas. The SWOT Analysis identifies the topics that represent break through technological advances that help implement the break-through innovation strategy.
[0056] Disruptive Strategy & Game Changer Strategy: These strategies involve application of technologies that are as yet not in the radar for the research sub area and the industry. As such topics involved in such strategic approaches are not show up in the SWOT analysis.
[0057] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[0058] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a

server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means, and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[0059] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[0060] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words

“comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[0061] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[0062] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

We Claim:
1. A processor implemented method (200) for Strength Weakness Opportunity and Threat (SWOT) analysis of a research area, the method comprising:
receiving, by one or more hardware processors (202) ,
a) a global topic set, generated by a topic model by processing global documents from a global corpus generated for a predefined time span, wherein the global topic set comprises a predefined number of topics derived from global documents comprising unstructured data, wherein each topic among the global topic set is assigned a topic score per global document to generate document-to-topics pairing for each of the global documents;
b) an enterprise topic set comprising one or more topics from among the global topic set, generated by the topic model, by processing enterprise documents comprising the unstructured data from an enterprise corpus generated for the predefined time span and belonging to a plurality of research areas of the enterprise, wherein each topic among the enterprise topic set is assigned the topic score per enterprise document to generate the document-to-topics pairing for each of the enterprise documents with the topics from the enterprise topic; and
c) a research area of interest among the plurality of research areas for performing the SWOT analysis;
mapping, by the one or more hardware processors, a plurality of topics identified from among the enterprise topic set and the global topic set to the research area of interest, wherein mapping is based on the topic score and an iterative topic allocation process (204);
computing, by the one or more hardware processors, a topic trend score for the plurality of topics mapped to the research area, wherein topic trend score is derived from percentage growth or decline in a topic frequency of the topic over per year over a period of time(206), wherein the

trend score comprises a) an enterprise trend score computed based on occurrence of the topic in publications present in the enterprise corpus, and b) a global trend score computed based on occurrence of the topic in publications present in the global corpus;
filtering, by the one or more hardware processors, the plurality of topics, based a topic relevance score for each of the plurality of topics to identify a set of significant topics in the research area of interest, wherein the set of significant topics have the topic relevance score above a predefined topic relevance score threshold (208), wherein the topic relevance score of each of the plurality of topics of the research area is obtained by:
a) ranking each of the plurality of topics in a given year based on the topic trend score of each topic; and
b) computing a mean ranking adding for each of the plurality of topics by adding a decaying weightage factor to the ranking of previous years of each of the plurality of topics, wherein the mean ranking provides the topic relevance score for each of the plurality of topics;
calculating, by the one or more hardware processors, a percentage topic intensity of each of the plurality of topics based on ratio of the topic frequency of a topic to sum of topic frequencies of the set of significant topics (210);
calculating, by the one or more hardware processors, a topic momentum for each of the set of significant topics by taking product of the percentage topic intensity and the enterprise topic trend score associated with each of the set of significant topics (212);
computing, by the one or more hardware processors, a correlation between growing trend of each of the set of significant topics at a global level with growing trend of corresponding each of the set of significant

topics within the enterprise by computing a Pearson Correlation Coefficient (PCC) for each of the topic based on the enterprise trend score and the global trend score for the topic (214); and
creating, by the one or more hardware processors, (a) SW quadrants providing Strengths (S) and Weaknesses (W) and (b) OT quadrants providing Opportunities (O), and Threats (T) the SWOT analysis of the set of significant topics (216), wherein the SW quadrants comprise the topic momentum plotted on Y-axis and the PCC plotted on X-axis, wherein topics in an upper right quadrant represent the Strengths (S) of the enterprise and topics in a lower right quadrant represent the Weaknesses (W) of the enterprise, and wherein a strength measure and a weakness measure is calculated by determining distance of the topic from origin of the SW quadrants with sign of the measure derived from the momentum indicating a strength or a weakness.
2. The method as claimed in claim 1, wherein generating the OT quadrants is based on an industry fitment score, wherein steps of generating the OT quadrant comprise:
receiving industry documents, comprising the unstructured data, for a plurality of industries of interest from industry-tracking websites and analyst reports collected over the predefined time span;
creating an industry topic set for each industry among a plurality of industries of interest, with each topic among the industry topic set having the topic score per industry document for each of the plurality of industries of interest, wherein the topic score is generated by the topic model by processing the industry documents corresponding to respective industry among the plurality of industries of interest;
filtering, from the industry topic set, topics that map to the research area of interest;

aggregating the topic score of each topic from among the filtered topics across the plurality the industry documents for each of the plurality of industries of interest;
computing the industry fitment score for topics for each industry by dividing the aggregated topic scores of each topic by a total number of industry documents for the industry and a constant scale factor derived empirically;
computing an overall industry fitment score for the topic among the filtered topics by aggregating the industry fitment score across the plurality of industries of interest; and
creating the OT quadrants for the SWOT analysis, with the Opportunities (O) represented by an upper right quadrant and the Threats (T) represented by a lower right quadrant, wherein the topics among the industry mapping topics are plotted as an opportunity or a threat based on industry fitment scores plotted on the X-axis and the Strengths and Weaknesses, obtained from SW quadrants, on the Y-axis.
3. The method as claimed in claim 1, wherein topics having the topic scores above a predefined threshold are identified in the global topic set, the enterprise topic set, and the industry topic set.
4. The method as claimed in claim 1, further comprising creating the SW quadrants and the OT quadrants for one or more competitors of interest by analyzing unstructured corpus obtained for the one or more competitors, wherein OT quadrants of the enterprise are compared with the OT quadrants for one or more competitors to derive comparative insights on whether a topic among the significant topics is an opportunity for the enterprise or a competitor among the one or more competitors is a threat for the enterprise for the topic.

5. The method as claimed in claim 4, wherein the opportunity for the topic is measured in terms of an opportunity measure, as distance of the topic lying in the upper right OT quadrant from origin divided by number of competitors having the same topic as strength, and the threat for the topic is measured in terms of a threat measure as distance of the topic lying in the lower right OT quadrants from the origin divided by number of competitors having the same topic as strength.
6. A system (100) for Strength Weakness Opportunity and Threat (SWOT) analysis of a research area, the system (100) comprising:
a memory (102) storing instructions;
one or more Input/Output (I/O) interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more I/O interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to: receive,
a) a global topic set, generated by a topic model by processing global documents from a global corpus generated for a predefined time span, wherein the global topic set comprises a predefined number of topics derived from global documents comprising unstructured data, wherein each topic among the global topic set is assigned a topic score per global document to generate document-to-topics pairing for each of the global documents;
b) an enterprise topic set comprising one or more topics from among the global topic set, generated by the topic model, by processing enterprise documents comprising the unstructured data from an enterprise corpus generated for the predefined time span and belonging to a plurality of research areas of the enterprise, wherein each topic among the enterprise topic set is assigned the topic score per enterprise document to generate the document-to-

topics pairing for each of the enterprise documents with the topics
from the enterprise topic; and
c) a research area of interest among the plurality of
research areas for performing the SWOT analysis;
map a plurality of topics identified from among the enterprise topic set and the global topic set to the research area of interest, wherein mapping is based on the topic score and an iterative topic allocation process;
compute a topic trend score for the plurality of topics mapped to the research area, wherein topic trend score is derived from percentage growth or decline in a topic frequency of the topic over per year over a period of time, wherein the trend score comprises a) an enterprise trend score computed based on occurrence of the topic in publications present in the enterprise corpus, and b) a global trend score computed based on occurrence of the topic in publications present in the global corpus;
filter the plurality of topics, based a topic relevance score for each of the plurality of topics to identify a set of significant topics in the research area of interest, wherein the set of significant topics have the topic relevance score above a predefined topic relevance score threshold, wherein the topic relevance score of each of the plurality of topics of the research area is obtained by:
a) ranking each of the plurality of topics in a given year based on the topic trend score of each topic; and
b) computing a mean ranking adding for each of the plurality of topics by adding a decaying weightage factor to the ranking of previous years of each of the plurality of topics, wherein the mean ranking provides the topic relevance score for each of the plurality of topics;

calculate a percentage topic intensity of each of the plurality of topics based on ratio of the topic frequency of a topic to sum of topic frequencies of the set of significant topics;
calculate, a topic momentum for each of the set of significant topics by taking product of the percentage topic intensity and the enterprise topic trend score associated with each of the set of significant topics;
compute a correlation between growing trend of each of the set of significant topics at a global level with growing trend of corresponding each of the set of significant topics within the enterprise by computing a Pearson Correlation Coefficient (PCC) for each of the topic based on the enterprise trend score and the global trend score for the topic; and
create (a) SW quadrants providing Strengths (S) and Weaknesses (W) and (b) OT quadrants providing Opportunities (O), and Threats (T) the SWOT analysis of the set of significant topics, wherein the SW quadrants comprise the topic momentum plotted on Y-axis and the PCC plotted on X-axis, wherein topics in an upper right quadrant represent the Strengths (S) of the enterprise and topics in a lower right quadrant represent the Weaknesses (W) of the enterprise, and wherein a strength measure and a weakness measure is calculated by determining distance of the topic from origin of the SW quadrants with sign of the measure derived from the momentum indicating a strength or a weakness.
7. The system as claimed in claim 6, wherein the one or more hardware processors are configured to generate the OT quadrants based on an industry fitment score, wherein steps of generating the OT quadrant comprise:
receiving industry documents, comprising the unstructured data, for a plurality of industries of interest from industry-tracking websites and analyst reports collected over the predefined time span;
creating an industry topic set for each industry among a plurality of industries of interest, with each topic among the industry topic set having

the topic score per industry document for each of the plurality of industries of interest, wherein the topic score is generated by the topic model by processing the industry documents corresponding to respective industry among the plurality of industries of interest;
filtering, from the industry topic set, topics that map to the research area of interest;
aggregating the topic score of each topic from among the filtered topics across the plurality the industry documents for each of the plurality of industries of interest;
computing the industry fitment score for topics for each industry by dividing the aggregated topic scores of each topic by a total number of industry documents for the industry and a constant scale factor derived empirically;
computing an overall industry fitment score for the topic among the filtered topics by aggregating the industry fitment score across the plurality of industries of interest; and
creating the OT quadrants for the SWOT analysis, with the Opportunities (O) represented by an upper right quadrant and the Threats (T) represented by a lower right quadrant, wherein the topics among the industry mapping topics are plotted as an opportunity or a threat based on industry fitment scores plotted on the X-axis and the Strengths and Weaknesses, obtained from SW quadrants, on the Y-axis.
8. The system as claimed in claim 6, wherein the one or more hardware processors are configured to identify topics having the topic scores above a predefined threshold in the global topic set, the enterprise topic set, and the industry topic set.
9. The system as claimed in claim 6, wherein the one or more hardware processors are further configured to create the SW quadrants and the OT quadrants for one or more competitors of interest by analyzing unstructured

corpus obtained for the one or more competitors, wherein OT quadrants of the enterprise are compared with the OT quadrants for one or more competitors to derive comparative insights on whether a topic among the significant topics is an opportunity for the enterprise or a competitor among the one or more competitors is a threat for the enterprise for the topic.
10. The system as claimed in claim 9, wherein the opportunity for the topic is measured in terms of an opportunity measure, as distance of the topic lying in the upper right of the OT quadrants from the origin divided by number of competitors having the same topic as strength, and the threat for the topic is measured in terms of a threat measure as distance of the topic lying in the lower right of the OT quadrants from the origin divided by number of competitors having the same topic as strength.