Sentiment Analysis of Cyberbullying Data in Social Media

TL;DR

This work developed a Recurrent Neural Network with Long Short-Term Memory (LSTM) cells, using different embeddings, and conducted a performance comparison between these two approaches to evaluate their effectiveness in sentiment analysis of Formspring Cyberbullying data.

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

Figures (2)

• Figure 1: OpenAI API Embedding genration
• Figure 2: RNN network with BERT/OpenAI API embeddings