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Automated Personality Prediction Using Social Media Data

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    Project Mart
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Introduction

The rise of social media has provided a rich source of data that can be used to infer various personal attributes, including personality traits. Automated personality prediction using social media data aims to extract meaningful insights about individuals' personalities without requiring them to take traditional personality tests. This project proposal focuses on developing a system that utilizes deep learning techniques to predict personality traits based on social media activity.

Background

Personality prediction from social media data has gained traction due to its potential applications in areas such as recruitment, marketing, and personal development. The Big Five personality traits model is commonly used in this field, providing a standardized framework for assessing personality. Recent advancements in natural language processing (NLP) and deep learning have enabled more accurate predictions by leveraging pre-trained language models like BERT, RoBERTa, and XLNet. These models can capture the semantic nuances of language, offering improved performance over traditional methods.

Project Objective

The primary objective of this project is to develop a robust personality prediction system that utilizes multiple social media platforms as data sources. By integrating advanced NLP techniques and deep learning architectures, the system aims to achieve high accuracy in predicting the Big Five personality traits.

Methodology

1. Data Collection and Preprocessing

  • Datasets: Use publicly available datasets such as MyPersonality from Facebook and Twitter datasets annotated by psychological experts.
  • Data Annotation: Ensure that the datasets are annotated with personality labels based on the Big Five traits.

2. Model Architecture

  • Multi-Model Deep Learning Architecture: Implement a combination of pre-trained language models (BERT, RoBERTa, XLNet) for feature extraction.
  • Ensemble Learning: Use model averaging techniques to combine predictions from multiple models for enhanced accuracy.

3. Training and Evaluation

  • Training: Train the models using cross-validation techniques to ensure robustness.
  • Evaluation Metrics: Assess model performance using accuracy, precision, recall, and F1-score.

Expected Outcomes

The proposed system is expected to outperform existing methods in terms of accuracy and reliability. By utilizing multiple social media data sources and advanced NLP techniques, the system should provide comprehensive personality profiles with minimal manual intervention.

Conclusion

This project seeks to advance the field of automated personality prediction by developing a state-of-the-art system that leverages social media data and cutting-edge deep learning techniques. The integration of multiple pre-trained language models is anticipated to significantly enhance predictive accuracy.

For further details on related research, please refer to the paper "Automated Personality Prediction Using Social Media Data," available at https://www.sciencedirect.com/science/article/pii/S1877050920307924.

Dataset Link: MyPersonality dataset can be accessed at https://www.kaggle.com/datasets/datasnaek/mbti-type.

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