FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(See section 10, rule 13)
1. Title of the Invention:
“SYSTEM AND METHOD FOR DETERMINING INFLUENCE OF AN ONLINE CONTENT ON A PRODUCT”
2. APPLICANT (S) -
(a) Name : Zensar Technologies Limited
(b) Nationality : Indian
(c)Address : Plot#4 Zensar Knowledge Park, MIDC, Kharadi,
Off Nagar Road, Pune, Maharashtra - 411014, India.
The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
The present invention relates to the field of data analysis, and more particularly to analyzing content obtained from online sources to determine their influence on a demand of a product and credibility of the online sources.
BACKGROUND OF INVENTION
Every major company decision, from financial planning to project execution, starts with a prediction of future sales, so demand forecast accuracy matters. Under-estimating demand means running out of product when customer demand is at its highest, costing the company immediate revenue and hurting the relationship with customer base. Over-estimating demand means companies have to invest upfront in a lot of extra inventory, which then can’t be quickly turned around into a profit.
Critical business assumptions like turnover, profit margins, cash flow, capital expenditure, risk assessment and mitigation plans, capacity planning, etc. are dependent on demand. As many internet users are growing daily, people rely more upon the information gathered online. Factors that may influence demand of a product includes information available on news websites, social media, blogs etc. Conventionally, the historical organizational sales data is utilized to develop an estimate of an expected forecast of customer demand.
However, since foreseeing demand forecast of a product only by the analysis of historical organizational sales data might not be highly accurate, techniques must be there that considers the impact of online sources on the product along with the analysis of organizational sales data. However, the technical challenge is analyzing huge amount of data associated with the online content which are regularly published on the online sources. Online content like news articles and blogs are published very frequently with informative content which might impact on the sales/demand of the product. Often the informative content may vary from one news article to another news article in spite being published on same news source. This variation may not only create a confusion but also impact the demand of the product. Analyzing these online contents and arriving at some useful and accurate information is a technical challenge.

There is, therefore, a need for a system and method that analyses the influence of online content available on online sources to forecast demand for products belonging to different categories. Further, there is, a need for a system and method that also determines the credibility of the online sources publishing the articles related to the product in order to have true analysis to forecast the demand of the product.
The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
SUMMARY OF THE INVENTION
The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages discussed throughout the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.
In one embodiment of the present disclosure, a method for determining influence of online contents on a product is disclosed. The method comprises extracting a set of relevant online contents, from a plurality of online contents stored in a plurality of data blocks, using one or more product related keywords. The set of relevant online content is published on an online source, having a previous credibility score, predicting a demand of the product during a predefined time-period. The method further comprises calculating a set of influence scores corresponding to the set of relevant online contents, wherein each influence score indicates positive impact or negative impact basis the influence score computed for the set of relevant online content. The method further comprises determining a positive proportion value and a negative proportion value, based on the set of influence scores, for the set of relevant online content. The method further comprises determining a demand change value for the product, wherein the demand change value is an actual demand of the product observed during the predefined time-period. The method further comprises corelating the set of influence scores, the positive proportion value, the negative

proportion value, and the demand change value among each other to determine a current credibility score of the online source. The method further comprises retuning the previous credibility score of the online source with the current credibility score indicating a current influence of the online source publishing the set of online relevant contents on the demand of the product.
In one embodiment of the present disclosure, a system for determining influence of an online content on a product is disclosed. The system comprises an extraction unit configured to extract a set of relevant online contents, from a plurality of online contents stored in a plurality of data blocks, using one or more product related keywords. The set of relevant online contents are published on an online source, having a previous credibility score, predicting a demand of the product during a predefined time-period. The system further comprises a calculation unit configured to calculate a set of influence scores corresponding to the set of relevant online contents, wherein each influence score indicates positive impact or negative impact for the set of relevant online content. The system further comprises a determination unit configured to determine a positive proportion value and a negative proportion value, based on the set of influence scores, for the set of relevant online contents and determine a demand change value for the product. The demand change value is an actual demand of the product observed during the predefined time-period. The system further comprises a correlation unit configured to corelate the set of influence scores, the positive proportion value, the negative proportion value, and the demand change value among each other to determine a current credibility score of the online source. The system further comprises a retuning unit configured to retune the previous credibility score of the online source with the current credibility score indicating a current influence of the online source publishing the set of online relevant contents on the demand of the product.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
BRIEF DESCITPION OF DRAWINGS

The embodiments of the disclosure itself, as well as a preferred mode of use, further objectives and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings. One or more embodiments are now described, by way of example only, with reference to the accompanying drawings in which:
Figure 1 shows an exemplary environment 100 of a system for determining influence of online content on a product, in accordance with an embodiment of the present disclosure;
Figure 2 shows a block diagram 200 illustrating a system for determining influence of online content on a product, in accordance with an embodiment of the present disclosure;
Figure 3 shows a method 300 for determining influence of online content on a product, in accordance with an embodiment of the present disclosure; and
Figure 4 shows a block diagram of an exemplary computer system 400 for implementing the embodiments consistent with the present disclosure.
The figures depict embodiments of the disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The foregoing has broadly outlined the features and technical advantages of the present disclosure in order that the detailed description of the disclosure that follows may be better understood. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure.
The novel features which are believed to be characteristic of the disclosure, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the

accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.
Disclosed herein is a system and method for determining influence of online content on a product. Demand forecasting lays the foundation for many critical business assumptions such as turnover, profit margins, cash flow, capital expenditure, and allows for estimating how many goods or services will sell and how much inventory needs to be ordered. In order to forecast demand or predicting risk, historical data or past organizational sales data on the market and past revenue is required, but the time span, the scope of the market, and other details can change the results. However, relying only on the analysis of past organizational sales data may not provide accurate results. Imagine if demand is predicted to grow, and the company is liberal with its yearly budgets as a result but demand actually shrinks. Thus, it is important to get an accurate prediction.
Media or online sources play a major role in influencing the demand of a product as billions of users are using the internet. The online content related to any product published on the online sources greatly influence the demand of the product. So, it becomes important to analyse the online content along with the historical data or past organizational sales data for accurate predictions. Taking for example, an organization such as Nestle, is into the manufacturing of a food product Maggi. Now, it becomes important for Nestle to forecast a demand for Maggi in a specific region, by not just analyzing past organizational sales data for that particular region but by also analyzing content obtained from various online sources. The combination of data obtained from various reliable online sources may thereby help the company in accurately determining the demand for the food product Maggi in upcoming period.
, For instance, in 2015, the news of imposing ban on Maggi noodles in many states of India due to the presence of Monosodium glutamate (MSG) and impermissible levels of lead in it has greatly influenced the sales of Maggi for many years. Demand of this food product was greatly declined. Thus, media and online sources have a great influence on the demand of the product. Further, it becomes also necessary to check the credibility of the online

source from which content is analysed to determine reliability of the online sources so as to get accurate results.
Nowadays, many businesses rely on artificial intelligence models to help in the demand forecast calculation. This makes the forecast more accurate and reliable while saving human time that would otherwise be spent on manual calculations.
The great thing about using artificial intelligence models for predicting is that once the model is built to calculate a specific formula, it can update predictions as time passes. That way, there is always a real-time prediction available that includes any new data, thereby making the analysis process more efficient.
The system disclosed herein therefore, analyses online content collected from various online sources by employing artificial intelligence to predict the influence of online content on the product in the near future. The system also provides highly accurate and time-efficient predictions by eliminating any errors that may emerge because of human mediation during the task of analyzing high-volumes of information got from different sources. The detailed working of the system has been explained in the upcoming paragraphs.
Figure 1 shows an exemplary environment 100 of a system for determining influence of online content on a product, in accordance with an embodiment of the present disclosure. It must be understood to a person skilled in art that the system may also be implemented in various environments, other than as shown in Fig. 1.
The detailed explanation of the exemplary environment 100 is explained in conjunction with Figure 2 that shows a block diagram 200 of a system 102 for determining influence of online content on a product, in accordance with an embodiment of the present disclosure. Although the present disclosure is explained considering that the system 102 is implemented on a server, it may be understood that the system 102 may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a cloud-based computing environment.

In one implementation, the system 102 may comprise an I/O interface 202, a processor 204, a memory 206 and the units 208. The memory 206 may be communicatively coupled to the processor 204 and the units 208. The data blocks 106 are stored in the memory 206. The significance and use of each of the stored quantities is explained in the upcoming paragraphs of the specification. The processor 204 may 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 processor 204 is configured to fetch and execute computer-readable instructions stored in the memory 206. The I/O interface 202 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 202 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 202 may facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 202 may include one or more ports for connecting many devices to one another or to another server.
In one implementation, the units 208 may comprise an extraction unit 210, a calculation unit 212, a determination unit 214, a correlation unit 216, and a retuning unit 218. According to embodiments of present disclosure, these units 210-218 may comprise hardware components like processor, microprocessor, microcontrollers, application-specific integrated circuit for performing various operations of the system 102. It must be understood to a person skilled in art that the processor 204 may perform all the functions of the units 210-218 according to various embodiments of the present disclosure.
Now referring back to Figure 1, the environment 100 shows a system 102 that the online contents from various online sources 1-n are received and stored in various data blocks 106 present in the memory 206 of the system 102. The online contents are received a particular duration or a time period, for example a day, week, month and year or any predefined time period. The working of the system 102 is explained with the help of an

example considering the current pandemic of coronavirus, which is basically a respiratory disease that impacts our lungs and in severe cases can cause oxygen levels to drop to dangerously low levels, making the disease fatal for the patient. Earlier (before covid), the sale of oxygen concentrators was not on high demand. But as covid cases are going up, the oxygen concentrator is among the most sought-after devices for oxygen therapy, especially among patients in home isolation and for hospitals running out of oxygen. Now suppose an organization named OxyCon wants to forecast the demand for the oxygen concentrators in a specific region such India for the month of June 2021. To make this forecast, the online contents of last three months (March 2021-May 2021) is received from various online sources 1-n for example, but not limited to ET Now, CNBC, BBC news, Reuters, Newyork times etc. to analyze 110 the impact on the demand of the product. The online contents i.e., news articles, blogs, media content for each day from (March 2021-May 2021) may be stored in the data blocks 106. In accordance with the exemplary environment, 200 news articles may be stored in the data blocks 106 which were published from March 2021 to May 2021. However, these news articles may be related to various fields like healthcare, electronics, food products, entertainment, pandemic etc. Hence, all the news articles may not be relevant while analyzing a particular product.
In next step, the extraction unit 210 extracts a set of the relevant online contents 108, from the online contents stored in the data blocks 106, using one or more product related keywords. The product for which the analysis is to be performed would be used as a query term to identify and extract a set of the relevant online contents 108 from the online contents stored in the data blocks 106. For example, if the product is oxygen concentrator, the stored online content would be searched for both the words "oxygen" & "concentrator" or even their synonyms/related keywords occurring together to identify the set of relevant online contents 108 related to the product. According to an embodiment, the extraction unit 210 may use a Natural Language Processing (NLP) technique for generating the product related keywords. In accordance with the exemplary environment, a total of 50 news articles from all the online sources 1-n are extracted related to the product oxygen concentrators from Mar 2021-May 2021. This way, a lot of unwanted data is filtered out which further helps the system 102 in performing the analysis at faster rate.

The set of relevant online contents 108 published on online sources 1-n are analyzed to predict the influence of online content on the demand of the product and the risks associated with it. The set of relevant online contents 108 extracted from various online data sources is further standardized by performing various techniques such as tokenization, lower casing, special character removal and applying language filter techniques to achieve uniformity in the extracted content. In next step, the calculation unit 212 calculates a set of influence scores corresponding to the set of relevant online contents 108. The set of influence scores calculated may be either a positive influence score or a negative influence score indicating a positive impact or a negative impact respectively of corresponding relevant online content on the demand of the product. The calculation unit 212 calculates the plurality of influence scores based on demand score, market analysis score, sentiment score and emotional score, which are described here in detail.
Calculating Demand Score
The calculation unit 212 analyses each relevant online content to identify one or more demand related keywords. In accordance with the exemplary environment, demand related keywords in a particular relevant online content may include words like "purchasing", "importance", "increased dependence" etc. After identifying the demand related keywords, weightages are assigned to them based on their importance using a ranking technique such as textrank technique. For example, the weightages assigned to "purchasing", "importance", "increased dependence" may be 0.6, 0.8, and 1.6 respectively. The calculation unit 212 then aggregates the assigned weightages to calculate a demand score for that relevant online content. In this case, the demand score for the relevant online content is "3", which is a "positive demand score".
Calculating Market Analysis Score
The calculation unit 212 further performs the market analysis using one or more tools and calculate market analysis score for the product. The market analysis is performed to analyse the product based different parameters like product category performance, brand performance and competitors' performance. For example, the calculation unit 212 may calculate different parameters during the market analysis. Considering an example of smartphone as a product sold by company "Smartylnc", the calculation unit 212 may

determine different values like "smartphone sales revenue", "market share", "users", "Smartylnc market share", and other 3 competitor company market share as vector. According to an embodiment, the different values calculated as vector format may comprise [409 billion, 3.3 billion, 85, 16, 20, 8, 3]. Based on these values, the calculation unit 212 calculates market score.
Calculating Sentiment Score
The calculation unit 212 is further configured to calculate a sentiment score of each relevant online content by applying sentiment analysis technique on each relevant online content. The sentiment score comprises at least one of a positive sentiment score, negative sentiment score, and a neutral sentiment score. For example, -0.2 indicates a negative sentiment score, whereas 0.4 indicates a positive sentiment score.
Calculating Emotional Score
The calculation unit 212 is further configured to calculate an emotional score by applying an emotional analysis technique on each relevant online content. The emotional score indicates the influence that the product will have on customer. There may be different emotions like Joy, Sad, Fear, Anxiety, Disgust which may be determined while performing the emotional analysis of the relevant online content. According to an embodiment, the calculation unit 212 may assign different values for different emotions which may be determined from the online content. For example, if the article recites that "There is fear among companies that due to import bans from China, the sale of products will be low in India", the calculation unit 212 may consider this under "Fear" or "Anxiety" emotions and accordingly generate the emotional score for that relevant online content.
Calculating Influence Score
Thus, the calculation unit 212 applies a regression model upon the demand score, the market score, the sentiment score, and the emotional score to determine the influence score of the relevant online content. The influence score calculated may be a positive influence score or a negative influence score. The positive influence score and the negative influence score indicates a positive impact and a negative impact respectively of corresponding relevant online content on the demand of the product. Considering the

example that after analyzing the "50" relevant documents, the calculation unit 212 comes up with "35" relevant online contents having the positive influence score and "15" relevant online contents having negative influence score.
Determining positive and negative proportion value
Based on the set of influence scores, the determining unit 214 determines a positive
proportion value and a negative proportion value for the set of relevant online contents 108. According to an embodiment, the positive proportion value is a ratio of number of relevant online content having the positive influence score to a total number of the set of relevant online content 108. Whereas the negative proportion value is a ratio of number of relevant online content having the negative influence score to the total number of the set of relevant online content 108.
Determining demand change value
The determining unit 214 is further configured to determine a demand change value
for the product. The demand change value is an actual demand of the product observed during the predefined time-period. In other words, the demand change value gives an actual figure about the sales (either up or decline) of the product during the predefined time-period being impacted due to the set of relevant online contents 108.
Now, it is necessary to determine the reliability of the online source on which the set of relevant online contents are published. This is important to check the reliability and accuracy of the online contents being published on the online source. If the online source is a reputed or well-known source, it is expected that the news articles published by them would be on correct information. However, such expectation cannot be taken as granted as these online source may also misuse the position. Hence, to keep on check on their credibility, the correlation unit 216 now corelates the set of influence scores, the positive proportion value, the negative proportion value, and the demand change value among each other to determine a current credibility score of the online source. According to an embodiment, the correlation unit 216 may use following technique for calculating the current credibility score of the online source.

For every online source say (S), initial credibility score of [0.5] is assigned for each product say (P). Credibility score is generated by using the below technique: CredibilityScore(S, P) = CredibilityScore(S, P) + [((proportion of positive) * (posinfluencescore) - (proportion of negative) * (neginfluence score) ]* (% change in observed demand))
The terms in the above technique are described below:
Credibility Score(S, P)→ Score of news source S with respect to product P;
Proportion of positive (positive proportion value) →total number of articles talking about "rise" in demand/ total articles;
Proportion of negative (negative proportion value) → total articles talking about "fall" in demand/ total articles;
Posinfluencescore (positive influence score) → aggregated influence score of all the "positive articles";
Neginfluencescore (negative influence score) → aggregated influence score of all the "negative articles";
Demand change - % change in demand value for the product during the predefined time period, for example tl - t0.
Referring back to the above example of 50 articles extracted as the set of online relevant online contents 108,10 articles are obtained from Online Sourcel, 20 articles are obtained from Online Source_2, another 20 articles are obtained from Online Source 3. Now considering the Online Sourcel, 6 articles have positive influence score and 4 articles have negative influence score. So, the proportion of positive (positive proportion value) and the proportion of negative (negative proportion value) would be 6/10 and 4/10 respectively.
Further, Posinfluencescore is calculated by aggregating the influence scores of the articles with positive influence scores and Neginfluencescore is calculated by aggregating the influence scores of the articles with negative influence scores. Considering Online Sourcel, say, individual positive influence scores of 6 positive articles are 3, 4, 2,

3, 4, 6. Hence, the Posinfluencescore i.e. the aggregated influence scores of the articles with positive influence scores is "22".
Similarly, say, influence scores of 4 articles with negative influence scores are -3, -4, -5, and -2 respectively. So, Neg_influence_score i.e. the aggregated influence scores of the articles with negative influence scores is "-14".
Further, the observed demand change i.e. percentage change in demand value for the product over a period of time is now considered. If the current period of time is tl and the initial time is tO then the percentage change in demand value for the product is calculated by (tl -tO). Say, if the value at tO is 40 and the value at tl is 100, then the %change in demand value for the product is 60%.
By calculating the above values i.e., "proportion of positive", "proportion of negative", "Pos_influence_score" "Neg_influence_score" the system 102 gets an insight at the online content level. Whereas, by calculating demand change value, the system 102 also know about real-world effect on the demand of the product. This way, the present disclosure is able to perform the analysis from different angles i.e. not only what the online content is projecting but also what is actually happening in the real-world with the product being analyzed. This further helps in improving system 102 efficiency by correlating predictive information with real-world information for arriving at some useful information about the product.
Using the above calculated values, the calculation unit 212 finally calculates the current credibility score for the Online Sourcel i.e., C1 current which comes around "17". Similarly, using the above technique, the current credibility scores for Online Source_2 and Online Source_3 may be calculated i.e. C2currentas "14", C3current, as "-20", for example.
Hence, the current credibility scores of Online Sourcel and 2 is positive and for the Online Source_3 is negative. Considering the current credibility score of Online Sourcel being a positive score i.e. "17", it indicates that articles published by them during the predefined time period correctly predicts the demand of the product. In this case, since 6 out 4 articles were talking about rise in demand, it is considered that the Online Sourcel

is biased towards the rise in the demand of the product. And with the current credibility score also coming as a positive score, the credibility of the Online Sourcel increases because the prediction of rise in demand matches with the actual demand information which also indicates rise in the demand. Thus, the current credibility score indicates the actual or true influence of the online data source on the demand of the product.
The system 102 further uses the current credulity score to "retune" the previous credibility score using the retuning unit 218. The retuning unit 218 analyses the currently generated credibility score vis-a-vis the previously assigned credibility score. Based on the analysis, the organization OxyCon may understand that in coming month of June 2021, the demand is going to increase and takes the necessary steps to cater the demand beforehand. By retuning, the system 102 also learns from time to time about the credibility of the online source. Thus, the system 102 backtrack its previous learning and either affirm or modify it based on the real world situation, thereby improving its efficiency while analyzing the online contents published on the online sources. According to an embodiment of the present disclosure, the above discussed units 210-218 may be dedicated hardware units capable of executing one or more instructions stored in the memory 206 for performing various operations of the system 102. In another embodiment, the units 210-218 may be software modules stored in the memory 206 which may be executed by the processor 204 for performing the operations of the system 102.
It may be understood by a skilled person, that the exemplary embodiment depicted in Figure 1 focusses on a single product belonging to a particular category. However, the demand forecast may be provided for different products or different categories such as automotive, beauty, bakery, dairy, personal care etc.
Figure 3 depicts a method 300 for determining influence of online content on a product, in accordance with an embodiment of the present disclosure.
As illustrated in Figure 3, the method 300 includes one or more blocks illustrating a method for determining influence of online content on a product. The method 300 may be described in the general context of computer executable instructions. Generally,

computer executable instructions may include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform specific functions or implement specific abstract data types.
The order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described.
At block 302, the method 300 may include extracting a set of relevant online contents, from a plurality of online contents stored in a plurality of data blocks, using one or more product related keywords. The set of relevant online contents are published on an online source 1-n predicting a demand of the product during a predefined time-period. As shown in Fig. 1, each online source 1-n may have previous credibility score assigned to them i.e. C1 prev, C2prev, and C2prev.
At block 304, the method 300 may include calculating a set of influence scores corresponding to the set of relevant online contents. Each influence score is either a positive influence score or a negative influence score indicating a positive impact or a negative impact respectively of corresponding relevant online content on the demand of the product.
At block 306, the method 300 may include determining a positive proportion value and a negative proportion value, based on the set of influence scores, for the set of relevant online contents.
At block 308, the method 300 may include determining a demand change value for the product, wherein the demand change value is an actual demand of the product observed during the predefined time-period.
At block 310, the method 300 may include correlating the set of influence scores, the positive proportion value, the negative proportion value, and the demand change value among each other to determine a current credibility score of the online source.

At block 312, the method 300 may include retuning the previous credibility score with the current credibility score indicating a current influence of the online source publishing the set of online relevant contents related on the demand of the product.
Computer System
Figure 4 illustrates a block diagram of an exemplary computer system 400 for implementing embodiments consistent with the present disclosure. It may be understood to a person skilled in art that the computer system 400 and its components is similar to the system 102 referred in Fig. 2. In an embodiment, the computer system 400 may be a peripheral device, which is used for determining influence of online content on a product. The computer system 400 may include a central processing unit ("CPU" or "processor") 402. The processor 402 may comprise at least one data processor for executing program components for executing user or system-generated processes. The processor 402 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.
The processor 402 may be disposed in communication with one or more input/output (I/O) devices via I/O interface 401. The I/O interface 401 may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE) or the like), etc. Using the I/O interface, the computer system 400 may communicate with one or more I/O devices.
In some embodiments, the processor 402 may be disposed in communication with a communication network 414 via a network interface 403. The network interface 403 may communicate with the communication network 414. The communication unit may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted

pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.1 la/b/g/n/x, etc.
The communication network 414 may be implemented as one of the several types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The communication network 414 may either be a dedicated network or a shared network, which represents an association of several types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network 414 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.
In some embodiments, the processor 402 may be disposed in communication with a memory 405 (e.g., RAM 412, ROM 413, etc. as shown in FIG. 4) via a storage interface 404. The storage interface 404 may connect to the memory 405 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (DDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.
The memory 405 may store a collection of program or database components, including, without limitation, user /application, an operating system, a web browser, mail client, mail server, web server and the like. In some embodiments, computer system may store user /application data, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as OracleR or SybaseR.
The operating system may facilitate resource management and operation of the computer system. Examples of operating systems include, without limitation, APPLE MACINTOSHR OS X, UNIXR, UNIX-like system distributions (E.G., BERKELEY

SOFTWARE DISTRIBUTIONTM (BSD), FREEBSDTM, NETBSDTM, OPENBSDTM, etc.), LINUX DISTRIBUTIONSTM (E.G., RED HATTM, UBUNTUTM, KUBUNTUTM, etc.), IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), APPLER IOSTM, GOOGLER ANDROIDTM, BLACKBERRYR OS, or the like. A user interface may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system, such as cursors, icons, check boxes, menus, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, APPLE MACINTOSHR operating systems, IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), UnixR X-Windows, web interface libraries (e.g., AJAXTM, DHTMLTM, ADOBE® FLASHTM, JAVASCRIPTTM, JAVATM, etc.), or the like.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. 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., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.
A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it will be clear that more than one device/article (whether they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether they cooperate), it will be clear that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.
Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.
Advantages of the embodiments of the present disclosure are illustrated herein:
The existing technologies utilize past organizational sales data to predict sale/risk of a product but the present disclosure focuses on the online sources having rich information.
Analyzing the online contents based on various parameters to determining the credibility of the online sources publishing the online contents. Reference Numerals:

Reference Numeral Description
100 Exemplary environment of a system for determining influence of online content on a product
102 System
1-n Online sources
106 Data Blocks
108 Set of relevant online contents
110 Analysis
200 Block diagram of the system
202 I/O Interface
204 Processor
206 Memory
208 Units
210 Extraction Unit
212 Calculation Unit
214 Determination Unit
216 Correlation Unit
218 Retuning Unit

We Claim:
1. A method (300) for determining influence of online content on a product, the method
comprises:
extracting (302) a set of relevant online contents (108), from a plurality of online contents stored in a plurality of data blocks (106), using one or more product related keywords, wherein the set of relevant online contents (108) are published on an online source, having a previous credibility score, predicting a demand of the product during a predefined time-period;
calculating (304) a set of influence scores corresponding to the set of relevant online contents (108), wherein each influence score indicates either positive impact or negative impact basis the influence score computed for the set of relevant online content (108);
determining (306) a positive proportion value and a negative proportion value, based on the set of influence scores, for the set of relevant online content (108);
determining (308) a demand change value for the product, wherein the demand change value is an actual demand of the product observed during the predefined time-period; and
corelating (310) the set of influence scores, the positive proportion value, the negative proportion value, and the demand change value among each other to determine a current credibility score of the online source; and
retuning (312) the previous credibility score of the online source with the current credibility score indicating a current influence of the online source publishing the set of online relevant contents on the demand of the product.
2. The method as claimed in claim 1, further comprises, after extracting, standardizing the set of relevant online contents by performing techniques comprises at least one of tokenization, lower casing, special character removal, and language filtering technique.
3. The method as claimed in claim 1, wherein each influence score corresponding to each relevant online content is calculated by:
calculating a demand score by:
identifying one or more demand related keywords, in each relevant online content, indicating information pertaining to the demand of the product;
assigning one or more weightages corresponding to the one or more demand related keywords; and
aggregating the one or more weightages resulting in either positive demand score or a negative demand score;
calculating a market analysis score for the product;
calculating a sentiment score by applying sentiment analysis technique on each relevant online content, wherein the sentiment score comprises at least one of a positive sentiment score, negative sentiment score, and a neutral sentiment score;

calculating an emotional score by applying an emotional analysis technique on each relevant online content; and
applying a regression model upon the demand score, the market score, the sentiment score, and the emotional score to determine the influence score comprising at least one of a positive influence score and a negative influence score.
.
4. The method as claimed in claim 1, wherein:
the positive proportion value is a ratio of number of relevant online content having the positive influence score to a total number of the set of relevant online contents; and
the negative proportion value is a ratio of number of relevant online content having the negative influence score to the total number of the set of relevant online contents.
5. The method as claimed in claim 1, wherein the online content comprises at least one of news articles, and online blogs, media content, and wherein the online source comprises at least one of online news websites and online news portals.
6. A system (102) for determining influence of online content on a product, the system comprises:
an extraction unit (210) configured to extract a set of relevant online contents (108), from a plurality of online contents stored in a plurality of data blocks (106), using one or more product related keywords, wherein the set of relevant online contents (108) are published on an online source, having a previous credibility score, predicting a demand of the product during a predefined time-period;
a calculation unit (212) configured to calculate a set of influence scores corresponding to the set of relevant online contents (108), wherein each influence score indicates either positive impact or negative impact basis the influence score computed for the set of relevant online content (108);
a determination unit (214) configured to:
determine a positive proportion value and a negative proportion value,
based on the set of influence scores, for the set of relevant online contents (108);
determine a demand change value for the product, wherein the demand
change value is an actual demand of the product observed during the predefined
time-period;
a correlation unit (216) configured to corelate the set of influence scores, the positive proportion value, the negative proportion value, and the demand change value among each other to determine a current credibility score of the online source; and
a retuning unit (218) configured to retune the previous credibility score of the online source with the current credibility score indicating a current influence of the online source publishing the set of online relevant contents on the demand of the product.

7. The system as claimed in claim 6, is further configured to standardize the set of relevant online contents, after being extracted, by using techniques comprising at least one of tokenization, lower casing, special character removal, language filtering technique.
8. The system as claimed in claim 6, wherein calculation unit to calculate each influence score corresponding to each relevant online content is configured to:
calculate a demand score by:
identify one or more demand related keywords, in each relevant online
content, indicating information pertaining to the demand of the product;
assign one or more weightages corresponding to the one or more demand
related keywords; and
aggregate the one or more weightages resulting in either positive demand
score or negative demand score;
calculate a market analysis score for the product:
calculate a sentiment score by applying sentiment analysis technique on each relevant online content, wherein the sentiment score comprises at least one of a positive sentiment score, negative sentiment score, and a neutral sentiment score;
calculate an emotional score by applying an emotional analysis technique on each relevant online content; and
apply a regression model upon the demand score, the sentiment score, and the emotional score to determine the influence score comprising at least one of a positive influence score and a negative influence score.
9. The system as claimed in claim 6, wherein:
the positive proportion value is a ratio of number of relevant online content having the positive influence score to a total number of the set of relevant online content; and
the negative proportion value is a ratio of number of relevant online content having the negative influence score to the total number of the set of relevant online content;
10. The system as claimed in claim 6, wherein the online content comprises at least one of
news articles, and online blogs, media content, and wherein the online source comprises at
least one of online news websites and online news portals.