1. A platform for detecting character personality traits, comprising: a user interface for receiving textual data associated with a character; a natural language processing module configured to process the textual data to extract linguistic features; a machine learning module configured to analyse the linguistic features to identify one or more personality traits associated with the character; a database for storing the identified personality traits; and an output module for presenting the identified personality traits.

2. The platform of claim 1, wherein the user interface allows multiple users to input textual data associated with the character, and the linguistic features and identified personality traits are stored in the database associated with the respective user.

3. The platform of claim 1, further comprising a visualization module for displaying the identified personality traits in a graphical format.

4. The platform of claim 1, wherein the textual data comprises dialogue spoken by the character.

4. The platform of claim 1, wherein the linguistic features comprise patterns of frequency of certain types of words, use of certain pronouns or adjectives, sentence length, contextual relationship, sentiment analysis, or other language processing techniques.

6. The platform of claim 1, wherein the natural language processing module is further configured to identify changes in the character's personality traits over time based on changes in the linguistic features of the textual data.

7. A method for detecting character personality traits using a platform, comprising the steps of: accessing the platform and entering textual data associated with a character through the user interface; processing the textual data using natural language processing techniques to extract linguistic features; analysing the linguistic features using a machine learning algorithm to identify one or more personality traits associated with the character; storing the identified personality traits in the database associated with the user; and presenting the identified personality traits through the output module.

8. The method of claim 7, further comprising repeating the steps over a period of time to track changes in the character's personality traits over time.

9. The method of claim 7, wherein the natural language processing techniques include part-of-speech tagging, sentiment analysis, and named entity recognition.

10. The method of claim 7, wherein the personality traits include but are not limited to: extraversion, agreeableness, conscientiousness, emotional stability, and openness. IDENTIFICATION OF CHARACTER PERSONALITY TRAIT BY USING THE ELEMENT OF NATURAL LANGUAGE PROCESSING Abstract A platform for identifying character personality features according to embodiments of the present disclosure may comprise a user interface for receiving textual data related to a character. In certain implementations, the textual data may additionally be processed by a natural language processing module that is set up to extract linguistic characteristics. One or more personality qualities associated with the character may be determined by an analysis of the character's language features by a machine learning module in certain embodiments. In certain embodiments, the uncovered character features are recorded in a database. An embodiment might additionally include an output module to display the uncovered character flaws. , Claims:Claims :

1. A platform for detecting character personality traits, comprising: a user interface for receiving textual data associated with a character; a natural language processing module configured to process the textual data to extract linguistic features; a machine learning module configured to analyse the linguistic features to identify one or more personality traits associated with the character; a database for storing the identified personality traits; and an output module for presenting the identified personality traits.

2. The platform of claim 1, wherein the user interface allows multiple users to input textual data associated with the character, and the linguistic features and identified personality traits are stored in the database associated with the respective user.

3. The platform of claim 1, further comprising a visualization module for displaying the identified personality traits in a graphical format.

4. The platform of claim 1, wherein the textual data comprises dialogue spoken by the character.

4. The platform of claim 1, wherein the linguistic features comprise patterns of frequency of certain types of words, use of certain pronouns or adjectives, sentence length, contextual relationship, sentiment analysis, or other language processing techniques.

6. The platform of claim 1, wherein the natural language processing module is further configured to identify changes in the character's personality traits over time based on changes in the linguistic features of the textual data.

7. A method for detecting character personality traits using a platform, comprising the steps of: accessing the platform and entering textual data associated with a character through the user interface; processing the textual data using natural language processing techniques to extract linguistic features; analysing the linguistic features using a machine learning algorithm to identify one or more personality traits associated with the character; storing the identified personality traits in the database associated with the user; and presenting the identified personality traits through the output module.

8. The method of claim 7, further comprising repeating the steps over a period of time to track changes in the character's personality traits over time.

9. The method of claim 7, wherein the natural language processing techniques include part-of-speech tagging, sentiment analysis, and named entity recognition.

10. The method of claim 7, wherein the personality traits include but are not limited to: extraversion, agreeableness, conscientiousness, emotional stability, and openness.

Description:IDENTIFICATION OF CHARACTER PERSONALITY TRAIT BY USING THE ELEMENT OF NATURAL LANGUAGE PROCESSING
Field of the Invention
[0001] The presently disclosed embodiments are related, in general, to language processing system. More particularly, the presently disclosed embodiments provide a platform for personality trait detection based on natural language processing (NLP).
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The concept of personality traits and categorization has been studied extensively in the field of psychology. The categorization of personality traits has been developed through various theoretical models, such as the Five-Factor Model (FFM) which categorizes personality traits into five broad dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. One of the most influential models of personality trait categorization is the Five-Factor Model (FFM), also known as the Big Five personality traits. This model categorizes personality traits into five broad dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The FFM has been extensively researched and validated in many cultures and languages.
[0004] There are also many assessment tools are disclosed in patent literature disclosed. Exemplary documents are discussed below.
[0005] The US20160098480A1 (By: CONDUENT BUSINESS SERVICES) - This disclosure provides a method, system and computer program product for classifying text according to one of a plurality of sentiments. According to an exemplary method, text is classified using two or more sentiment classifiers which are tuned to distinct author profile traits and the resulting scores are combined using a normalized weighted function to produce a final resulting classification score.
[0006] The WO0199405A2 (By: ADVISORTEAM COM) - Method and system for determining personal characteristics of an individual or group and using same to provide personalized advice or services. The system dynamically incorporates several personality dimensions, life style, quality of life, cultural context, demographics, and psychographics, as requested by the test administrator or individual user, and controls and standardizes the testing protocol, and retains test data in such a way that individuals and non-professional users can reliably self-administer the tests, save their test results in a system database, and use the results to obtain personality-based advice, content, and people-matching services from a system proprietor.
[0007] The EP2153348A1 (By: MOTOROLA MOBILITY) - The invention generates a user profile for applications or services. Initially, a first user profile is generated at a first user device (101) for a first user. The first user device (101) stores user contacts for the first user, with the user contacts being associated with a social network of the first user. A set of user devices (103, 105, 107) associated with a set of the stored user contacts is then determined and at least part of the first user profile is transmitted to this set of user devices (103, 105, 107). At least one user device (103, 105, 107) generates user profile feedback which is transmitted back to the first user device (101). The first user device (101) then modifies the first user profile in response to the received user profile feedback. The invention allows a fast generation of an initial user profile with improved accuracy.
[0008] US20160239573A1 (By: XEROX) - According to embodiments illustrated herein there is provided a method for determining a psychological type of a user. The method includes determining a first score for the user based on a profile of the user on a social media platform. Further, a second score is determined for the user based on activities of the user on the social media platform. Thereafter, a third score is determined for the user based on context of conversations of the user on the social media platform, which is determined based on a part of speech of each word in the conversations using a context database. Each word is categorized based on at least the part of speech associated with the word. The third score is determined based on the categorization. The psychological type of the user is determined based on the first score, the second score, and the third score.
[0009] The known assessment techniques dependent on self-report questionnaires, interviews, and behavioural observations. However, these assessment tools are not efficient and required manual screening. Thus, there is need of improvised technique for trait categorization.

Summary
[00010] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00011] The following paragraphs provide additional support for the claims of the subject application.
[00012] The presently disclosed embodiments are related, in general, to language processing system. More particularly, the presently disclosed embodiments provide a platform for personality trait detection based on natural language processing (NLP).

[00013] Embodiments of the present disclosure may include a platform for detecting character personality traits, including a user interface for receiving textual data associated with a character. Embodiments may also include a natural language processing module configured to process the textual data to extract linguistic features. Embodiments may also include a machine learning module configured to analyse the linguistic features to identify one or more personality traits associated with the character. Embodiments may also include a database for storing the identified personality traits. Embodiments may also include an output module for presenting the identified personality traits.
[00014] In some embodiments, the user interface allows multiple users to input textual data associated with the character, and the linguistic features and identified personality traits may be stored in the database associated with the respective user. In some embodiments, the platform may include a visualization module for displaying the identified personality traits in a graphical format.
[00015] In some embodiments, the textual data may include dialogue spoken by the character. In some embodiments, the linguistic features may include patterns of frequency of certain types of words, use of certain pronouns or adjectives, sentence length, contextual relationship, sentiment analysis, or other language processing techniques. In some embodiments, the natural language processing module may be further configured to identify changes in the character's personality traits over time based on changes in the linguistic features of the textual data.
[00016] Embodiments of the present disclosure may also include a method for detecting character personality traits using a platform, wherein the method including the steps of accessing the platform and entering textual data associated with a character through the user interface. Embodiments may also include processing the textual data using natural language processing techniques to extract linguistic features.
[00017] Embodiments may also include analysing the linguistic features using a machine learning algorithm to identify one or more personality traits associated with the character. Embodiments may also include storing the identified personality traits in the database associated with the user. Embodiments may also include presenting the identified personality traits through the output module.
[00018] In some embodiments, the method may include repeating the steps over a period of time to track changes in the character's personality traits over time. In some embodiments, the natural language processing techniques include part-of-speech tagging, sentiment analysis, and named entity recognition. In some embodiments, the personality traits include but may be not limited to extraversion, agreeableness, conscientiousness, emotional stability, and openness.
Brief Description of the Drawings
[00019] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00020] FIG. 1 is a block diagram illustrating a platform for detecting character personality traits, according to some embodiments of the present disclosure.
[00021] FIG. 2 is a flowchart illustrating a method for detecting character personality traits, according to some embodiments of the present disclosure.
Detailed Description
[00022] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized, and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00023] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00024] The presently disclosed embodiments are related, in general, to language processing system. More particularly, the presently disclosed embodiments provide a platform for personality trait detection based on natural language processing (NLP).
[00025] FIG. 1 is a block diagram that describes a platform 100 for detecting character personality traits, according to some embodiments of the present disclosure. In some embodiments, the platform 100 may include a user interface 110 for receiving textual data associated with a character, a natural language processing module 120 configured to process the textual data to extract linguistic features, a machine learning module 130 configured to analyse the extracted linguistic features to identify one or more personality traits associated with the character, a database 140 for storing the identified personality traits, and an output module 150 for presenting the identified personality traits.
[00026] In some embodiments, the user interface 110 may allow multiple users to input textual data associated with the character, and the linguistic features and identified personality traits may be stored in the database 140 associated with the respective user. In some embodiments, the platform 100 may include a visualization module for displaying the identified personality traits in a graphical format. In some embodiments, the textual data may include dialogue spoken by the character.
[00027] In some embodiments, the linguistic features may also include patterns of frequency of certain types of words, use of certain pronouns or adjectives, sentence length, contextual relationship, sentiment analysis, or other language processing techniques. In some embodiments, the natural language processing module 120 may be further configured to identify changes in the character's personality traits over time based on changes in the linguistic features of the textual data.
[00028] FIG. 2 is a flowchart that describes a method for detecting character personality traits, according to some embodiments of the present disclosure. In some embodiments, at 210, the method may include accessing the platform and entering textual data associated with a character through the user interface. At 220, the method may include processing the textual data using natural language processing techniques to extract linguistic features. At 230, the method may include analysing the linguistic features using a machine learning algorithm to identify one or more personality traits associated with the character. At 240, the method may include storing the identified personality traits in the database associated with the user. At 250, the method may include presenting the identified personality traits through the output module.
[00029] In some embodiments, the method may include repeating the steps over a period of time to track changes in the character's personality traits over time. In some embodiments, the natural language processing techniques includes part-of-speech tagging, sentiment analysis, and named entity recognition. In some embodiments, the personality traits may include but may be not limited to, the method may include performing one or more additional steps. Extraversion, agreeableness, conscientiousness, emotional stability, and openness.
[00030] The user interface 110 is part of the platform 100 that may be used to identify the personality features of characters. The natural language processing module 120 that is set up to process the textual data in order to extract linguistic aspects is another component that may be included in embodiments. The machine learning module 130 that is designed to evaluate the character's linguistic data in order to determine one or more personality traits associated with the character may also be included in some embodiments. The database 140 for recording the various characteristics of a person's personality may also be included in certain embodiments. The output module 150 that displays the characteristics of the detected personalities may also be included in embodiments.
[00031] In some implementations, the user interface 110 provides numerous users with the ability to enter textual data connected with the character. Moreover, the linguistic characteristics and recognised personality traits may be saved in the database 140 that is associated with the user. In some implementations, the platform 100 may be equipped with a visualisation module that provides a graphical representation of the characteristics of personality that have been determined.
[00032] In some implementations, the textual data could consist of the character's dialogue that they've been speaking. The linguistic features can include patterns of frequency of certain types of words, usage of certain pronouns or adjectives, sentence length, contextual relationship, sentiment analysis, or other language processing techniques in some implementations. In some implementations, the natural language processing module 120 can be further programmed to identify shifts in the character's personality traits over time based on variations in the linguistic characteristics of the textual data. This can be done, for example, if the character's name is changed over the course of the story.
[00033] Accessing the platform 100 and entering textual data associated with a character through the user interface 110 are two steps that may be included in embodiments of the present disclosure that relate to a method for detecting character personality traits using a platform. Processing the textual data using natural language processing methods in order to extract linguistic aspects is another possibility that might be included in embodiments.
[00034] In certain embodiments, it is also possible to determine one or more personality qualities associated with the character by conducting an analysis of the character's linguistic properties using a machine learning algorithm. The recognised characteristics of the user's personality might also be saved in the database 140 connected with the user if the embodiment so specifies. The recognised characteristics of the person's personality might likewise be presented via the output module 150 in certain embodiments.

[00035] In some implementations of the approach, one of the stages may include doing the steps many times over the course of some amount of time in order to monitor how the personality traits of the character evolve over time. The natural language processing methods may vary depending on the implementation, but they can include things like part-of-speech tagging, sentiment analysis, and named entity recognition. Extraversion, agreeableness, conscientiousness, emotional stability, and openness are some of the personality qualities that may be included in some embodiments; however, these characteristics are not restricted to them.
[00036] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00037] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00038] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00039] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00040] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. A platform for detecting character personality traits, comprising:

a user interface for receiving textual data associated with a character;
a natural language processing module configured to process the textual data to extract linguistic features;
a machine learning module configured to analyse the linguistic features to identify one or more personality traits associated with the character;
a database for storing the identified personality traits; and
an output module for presenting the identified personality traits.

2. The platform of claim 1, wherein the user interface allows multiple users to input textual data associated with the character, and the linguistic features and identified personality traits are stored in the database associated with the respective user.

3. The platform of claim 1, further comprising a visualization module for displaying the identified personality traits in a graphical format.

4. The platform of claim 1, wherein the textual data comprises dialogue spoken by the character.

4. The platform of claim 1, wherein the linguistic features comprise patterns of frequency of certain types of words, use of certain pronouns or adjectives, sentence length, contextual relationship, sentiment analysis, or other language processing techniques.

6. The platform of claim 1, wherein the natural language processing module is further configured to identify changes in the character's personality traits over time based on changes in the linguistic features of the textual data.

7. A method for detecting character personality traits using a platform, comprising the steps of:

accessing the platform and entering textual data associated with a character through the user interface;
processing the textual data using natural language processing techniques to extract linguistic features;
analysing the linguistic features using a machine learning algorithm to identify one or more personality traits associated with the character;
storing the identified personality traits in the database associated with the user; and
presenting the identified personality traits through the output module.

8. The method of claim 7, further comprising repeating the steps over a period of time to track changes in the character's personality traits over time.

9. The method of claim 7, wherein the natural language processing techniques include part-of-speech tagging, sentiment analysis, and named entity recognition.

10. The method of claim 7, wherein the personality traits include but are not limited to: extraversion, agreeableness, conscientiousness, emotional stability, and openness.

IDENTIFICATION OF CHARACTER PERSONALITY TRAIT BY USING THE ELEMENT OF NATURAL LANGUAGE PROCESSING
Abstract
A platform for identifying character personality features according to embodiments of the present disclosure may comprise a user interface for receiving textual data related to a character. In certain implementations, the textual data may additionally be processed by a natural language processing module that is set up to extract linguistic characteristics. One or more personality qualities associated with the character may be determined by an analysis of the character's language features by a machine learning module in certain embodiments. In certain embodiments, the uncovered character features are recorded in a database. An embodiment might additionally include an output module to display the uncovered character flaws. , Claims:Claims
I/We Claim:
1. A platform for detecting character personality traits, comprising:

a user interface for receiving textual data associated with a character;
a natural language processing module configured to process the textual data to extract linguistic features;
a machine learning module configured to analyse the linguistic features to identify one or more personality traits associated with the character;
a database for storing the identified personality traits; and
an output module for presenting the identified personality traits.

2. The platform of claim 1, wherein the user interface allows multiple users to input textual data associated with the character, and the linguistic features and identified personality traits are stored in the database associated with the respective user.

3. The platform of claim 1, further comprising a visualization module for displaying the identified personality traits in a graphical format.

4. The platform of claim 1, wherein the textual data comprises dialogue spoken by the character.

4. The platform of claim 1, wherein the linguistic features comprise patterns of frequency of certain types of words, use of certain pronouns or adjectives, sentence length, contextual relationship, sentiment analysis, or other language processing techniques.

6. The platform of claim 1, wherein the natural language processing module is further configured to identify changes in the character's personality traits over time based on changes in the linguistic features of the textual data.

7. A method for detecting character personality traits using a platform, comprising the steps of:

accessing the platform and entering textual data associated with a character through the user interface;
processing the textual data using natural language processing techniques to extract linguistic features;
analysing the linguistic features using a machine learning algorithm to identify one or more personality traits associated with the character;
storing the identified personality traits in the database associated with the user; and
presenting the identified personality traits through the output module.

8. The method of claim 7, further comprising repeating the steps over a period of time to track changes in the character's personality traits over time.

9. The method of claim 7, wherein the natural language processing techniques include part-of-speech tagging, sentiment analysis, and named entity recognition.

10. The method of claim 7, wherein the personality traits include but are not limited to: extraversion, agreeableness, conscientiousness, emotional stability, and openness.