Comparative Analysis of Neural Network-Based Models for Sentiment Analysis on Twitter Data

Sentiment analysis of customer reviews available on the Twitter platform is of great business importance for business growth. People in daily life widely use Twitter to express their reactions. This medium provides a vast amount of public data for analysis. In this research, we employ Neural Network-Based Models (LSTM, CNN, and BERT) to classify tweets into positive, negative, or neutral sentiments. Our research showed that the BERT model performed better than other models in terms of both accuracy and generalization. The 280-character limit of Twitter makes it suitable for sentiment analysis due to its unique features and social influence. This study highlights the role of deep learning in analyzing customer sentiment and its applications in monitoring industry trends and customer satisfaction.