A Transformer Based Model for Twitter Sentiment Analysis using RoBERTa

In recent years, social media platforms, particularly twitter, have emerged as crucial sources of public opinion and sentiment. Analyzing sentiment on twitter data presents a significant challenge due to the platform's inherent characteristics, such as brevity, informality, and the prevalence of slang and emojis. This research paper proposes a method for twitter sentiment analysis by leveraging the power of a transformer-based model called RoBERTa. The proposed strategy employs RoBERTa due to its exceptional performance in various natural language processing tasks. Our system captures intricate contextual information and semantic nuances in tweets, making it well-suited for sentiment analysis on this challenging platform. To build an effective sentiment analysis system, the architecture is fine-tuned using a large corpus of twitter data, annotated with sentiment labels. Additionally, we explore various strategies to handle the unique characteristics of twitter data, including tokenization, handling hashtags, user mentions, and URLs, as well as the incorporation of emojis and emoticons. We compare the performance of our model with three other standard machine learning and deep learning models, such as Decision Tree (DT), Support Vector Machine (SVM), and Long Short Term Memory (LSTM) in order to show that our model is superior at correctly analyzing twitter sentiment. The model showcases an exceptional accuracy of 96.78%, highlighting its effectiveness in understanding and classifying sentiment within the context of tweets.