Enhancing sentiment analysis accuracy on social media comments using a tuned BERT model

Sentiment analysis of social media comments is essential for real-time comprehension of user input and public opinion. To facilitate finding relevant news and views on social networks, this study proposes a model that categorizes search results by classifying sentiments in social comments and news articles. The research explores how a tuned BERT (Bidirectional Encoder Representations from Transformers) model can enhance sentiment analysis accuracy. The process begins with preprocessing comments to remove unnecessary elements, followed by feature extraction and text classification using trained models. The study compares the tuned BERT model’s performance against established models like Support Vector Machines (SVM), Naive Bayes, Long Short-Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN). Using the Multi-Class Sentiment Analysis Dataset (MCSAD), the models…