Published Paper Details:

Social media platforms have emerged as a vital source of customer input and public opinion in the current digital era. When trying to glean relevant data from the vast amount of unstructured comments on these sites, it may be quite difficult. Due to the inefficiency of traditional manual sentiment analysis techniques, more advanced methods are now required. Recent developments in Natural Language Processing (NLP), in particular the application of neural network architectures such as Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT), have demonstrated significant promise in enhancing the precision and effectiveness of sentiment analysis. These models are perfect for large-scale comment analysis because they are very good at interpreting the mood, context, and subtleties of language used in social media comments. This study investigates the classification and analysis of social media comments using BERT, LSTM, and GRU, offering important insights into public opinion on a range of subjects. The results demonstrate how well these models capture trends, spot new problems, and support enterprises in making data-driven decisions. Additionally, by integrating these cutting-edge models, social media comments may be processed in real-time, empowering businesses to proactively address new trends or problems. The interpretability of intricate models and the scalability to manage very big datasets are still problems, though.