The present disclosure relates to systems, methods, and non-transitory computer-readable media for human personality prediction by analyzing information collected from different sources such as social media, call detail record (CDR), email etc. using DISC (dominance, inducement, submission, and compliance) profiling and Big Five personality techniques (openness, conscientiousness, extraversion, agreeableness, and neuroticism). Embodiments in accordance with the present disclosure are further capable of using a self-learning model which learns from user response to the prediction.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for predicting a personality of at least one human subject, the method comprising: receiving data associated with the at least one human subject from one or more sources; clustering the data based on one or more topics of interest of the at least one human subject using one or more topic modeling algorithms; predicting at least one high level personality trait associated with the at least one human subject by analyzing the clustered data, the at least one high level personality trait being one of one or more high level personality traits defined by a first model; and predicting at least one personality profile by classifying the at least one high level personality trait into one or more granular level personality traits defined by a second model, the classifying being based on clustered data, wherein at least one of the receiving data, the clustering the data, the predicting at least one first personality, and the predicting at least one second personality is performed by a processor. 2. The method of claim 1, further comprising assigning a score to each of the one or more granular level personality traits based on emotion attached to the at least one topic of interest. 3. The method of claim 2, wherein the emotion attached to the at least one topic of interest is determined based on frequency of talking about the at least one topic of interest by the at least one human subject across the one or more sources. 4. The method of claim 1, further comprising performing a people search based on the at least one high level personality trait, the at least one personality profile, demographic distribution, gender, and the at least one topic of interest. 5. The method of claim 4, further comprising determining a confidence level for result associated with the people search, the confidence level indicative of the accuracy of the result. 6. The method of claim 1, further comprising generating a multi-dimensional hierarchical correlation matrix of one or more human subjects of the at least one human subject based on at least one of the one or more topics of interest, gender, and demography. 7. The method of claim 1, wherein receiving the data associated with the at least one human subject from the one or more sources comprises: retrieving the data from the one or more sources; performing language identification on the data; cleaning and filtering the data by removing unwanted characters and notation; partitioning the data based on demography, culture, and gender; and transforming the data to at least one format. 8. The method of claim 1, further comprising monitoring response of a user to the predicted at least one high level personality trait and the predicted at least one personality profile using self-learning techniques. 9. The method of claim 1, wherein the first model is DISC (dominance, inducement, submission, and compliance) model. 10. The method of claim 1, wherein the second model is big five model (openness, conscientiousness, extraversion, agreeableness, and neuroticism). 11. A system for predicting a personality of at least one human subject, the system comprising: one or more hardware processors; and a computer-readable medium storing instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations comprising: receiving data associated with the at least one human subject from one or more sources; clustering the data based on one or more topics of interest of the at least one human subject using one or more topic modeling algorithms; predicting at least one high level personality trait associated with the at least one human subject by analyzing the clustered data, the at least one high level personality trait being one of the one or more high level personality traits defined by a first model; and predicting at least one personality profile by classifying the at least one high level personality trait into one or more granular level personality traits defined by a second model, the classifying being based on clustered data. 12. The system of claim 11, wherein a score is assigned to each of the one or more granular level personality traits based on emotion attached to the at least one topic of interest. 13. The system of claim 12, wherein the emotion attached to the at least one topic of interest is determined based on frequency of talking about the at least one topic of interest by the at least one human subject across the one or more sources. 14. The system of claim 11, wherein a people search is performed based on at least one of a high level personality trait, the at least one personality profile, demographic distribution, gender, and the at least one topic of interest. 15. The system of claim 14, wherein a confidence level is determined for result associated with the people search, the confidence level indicative of accuracy of the result. 16. The system of claim 11, wherein a multi-dimensional hierarchical correlation matrix of one or more human subjects of the at least one human subject is generated based on at least one of the one or more topics of interest, gender, and demography. 17. The system of claim 12, wherein receiving the data associated with the at least one human subject from the one or more sources comprises: retrieving the data from the one or more sources; performing language identification on the data; cleaning and filtering the data by removing unwanted characters and notation; partitioning the data based on demography, culture, and gender; and transforming the data to at least one format. 18. The system of claim 11, wherein response of a user to the predicted at least one high level personality trait and the predicted at least one personality profile is monitored using self-learning techniques. 19. A non-transitory computer-readable medium storing instructions for predicting a personality of at least one human subject that, when executed by a processor, cause the processor to perform operations comprising: receiving data associated with the at least one human subject from one or more sources; clustering the data based on one or more topics of interest of the at least one human subject using one or more topic modeling algorithms; predicting at least one high level personality trait associated with the at least one human subject by analyzing the clustered data, the at least one high level personality trait being one of the one or more high level personality traits defined by a first model; and predicting at least one personality profile by classifying the at least one high level personality trait into one or more granular level personality traits defined by a second model, the classifying being based on clustered data. 20. The non-transitory computer-readable medium of claim 19, wherein a score is assigned to each of the one or more granular level personality traits based on emotion attached to the at least one topic of interest.