R Hemanth

Bio: R Hemanth is an academic researcher from Nitte Meenakshi Institute of Technology. The author has contributed to research in topics: Personality Assessment Inventory & Personality. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Machine intelligence based personality prediction using social profile data

Abstract: Social network usage is growing exponentially every day. Different information are typically exchanged via social media platforms such as Facebook. User knowledge and what they have conveyed through changes in status are useful for learning about the behavior and human personality assessment. This work aims at setting up a framework that can predict the individual's personality based on Facebook user details. In order to analyze the individual's personality, big five model is used. The aim of this research is to predict the personality of user by using the status information present in their social media profile. Based on the analysis result, the user's personality is further classified into one of the categories present in the OCEAN model. The accuracy of personality prediction achieved by using Random Forest Classifier is 64.25%. The mean squared error is achieved using random forest regressor is 5.25.

Text-Based Automatic Personality Recognition: Recent Developments

Abstract: The use of computation in personality recognition has been explored for several decades now. As such, it is possible to derive personality from the data available on social media, telecommunication signals, and every signal obtained from human–machine interaction. Personality computation has been explored in two major domains: social signal processing and human–computer interaction. Automatic personality trait recognition from textual context is an emerging research topic that has gotten considerable attention in the area of natural language processing (NLP). In this survey, we reviewed the existing works in the field of automatic personality detection from texts and provided a comparative analysis. We identified some open research gaps and discussed major issues presented in existing literature, including issues with current datasets, techniques, personality features, and personality models employed, as well as how they can be bettered in the future.

A research attempt to predict and model personalities through users' social media details

Abstract: Users of products and services, as human beings have a wide range of personalities. This is being experienced right from the initial days of e-commerce and m-commerce in India. In this research an attempt has been made to predict personalities using MBTI (Myers Briggs Type Indicator) based approach making use of natural language based processing, machine learning and transformer based modelling. As each human being is unique and exhibits different personality trait, therefore it is impractical to offer a generalized treatment for all users. But it is possible to categorize individuals, in terms of their defining characteristics based on MBTI based approach, which groups personalities/users into 16 groups and thus helps in predicting personalities. In this study authors made an attempt to extract social media based information of users through their accounts to characterize users into one of the 16 MBTI personality types. For this prediction and modelling, authors made use of pre-processed data from Kaggle, which was then fed into the transformer for modelling/processing. Based on the information it gets, like comments, post captions, reviews, etc., the transformer is fine-tuned to predict the user's personality. The required qualities of the model were taken into account while coding the transformer's parameters. Additionally, an attempt is also made to compare the outcomes of two trained transformer models. Authors report that the prediction accuracy of their modelling as 64%, outperforming all other models used. The testing data had a 76% precision.