Akshata S Bhayyar, Kiran P, Sushma Bylaiah
DOI: 10.5110/77. 1046 Page: 102-120 Vol: 18 Issue: 09 Year: 2023
Recently, cognitive-based sentiment analysis has drawn a lot of attention because it focuses on automatically identifying user behaviours like personality characteristics from online social media text. In order to demonstrate the effectiveness of the suggested model for eight key personality traits (Introversion-Extroversion, Intuition- Sensing, Thinking-Feeling, and Judging-Perceiving), we present a hybrid Deep Learning-based model made up of Convolutional Neural Networks with Long Short-Term Memory. On the basis of audio and video recordings of human faces, we provide a model for the identification of personality traits. A web-based platform is created to gather the dataset, allowing users to record voice and video using a microphone and webcam, respectively. The dataset contains videos and audio clips of people of various ages and genders. Applying the proposed CNN+LSTM model on the considered dataset we could achieve an accuracy of 87.07%.
Cognitive Psychology, Personality traits, Convolutional Neural Networks, Long Short-Term Memory, Myers-Briggs Type Indicator (MBTI)
Received: 22 October 2023
Accepted: 05 November 2023
Published: 13 November 2023