Sentiment Analysis of Cyberbullying Data in Social Media

TL;DR

This paper explores deep learning methods, specifically LSTM with BERT and OpenAI embeddings, to detect cyberbullying in social media, aiming to improve sentiment analysis accuracy for identifying at-risk individuals.

Contribution

It introduces a novel comparison of BERT and OpenAI embeddings within an LSTM framework for cyberbullying sentiment detection on social media data.

Findings

BERT embeddings outperform OpenAI embeddings in detection accuracy

LSTM models effectively identify cyberbullying traces

OpenAI embeddings show competitive performance with less computational cost

Abstract

Social media has become an integral part of modern life, but it has also brought with it the pervasive issue of cyberbullying a serious menace in today's digital age. Cyberbullying, a form of harassment that occurs on social networks, has escalated alongside the growth of these platforms. Sentiment analysis holds significant potential not only for detecting bullying phrases but also for identifying victims who are at high risk of harm, whether to themselves or others. Our work focuses on leveraging deep learning and natural language understanding techniques to detect traces of bullying in social media posts. We developed a Recurrent Neural Network with Long Short-Term Memory (LSTM) cells, using different embeddings. One approach utilizes BERT embeddings, while the other replaces the embeddings layer with the recently released embeddings API from OpenAI. We conducted a performance comparison between these two approaches to evaluate their effectiveness in sentiment analysis of Formspring Cyberbullying data. Our Code is Available at https://github.com/ppujari/xcs224u