Leveraging Deep Learning to Predict Personality Traits using Facebook Posts and Tweets
The PersonaNet project focuses on harnessing deep learning techniques to predict an individual's personality traits based on their Facebook posts and Tweets. This prediction is aligned with the Myers Briggs Type Indicator (MBTI), a widely recognized personality type system that categorizes individuals into 16 distinct personality types.
- Employed Natural Language Processing (NLP) techniques to preprocess the textual data derived from social media interactions.
- Employed a range of classical machine learning models, including Support Vector Machine (SVM), Random Forest, and XGBoost, to capture personality patterns in the data.
- Utilized sophisticated deep learning models such as Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) to extract intricate patterns and contextual information from the text data.
- Achieved an accuracy rate of 66.86% using XGBoost.
- Leveraged BERT to achieve an impressive accuracy of 77.73%.
The PersonaNet project exemplifies the fusion of text analysis and machine learning to gain profound insights into an individual's personality traits. By amalgamating advanced methodologies, we've provided a platform for unveiling hidden facets of personality through online interactions.