Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media

The field of sentiment analysis is a crucial aspect of natural language processing (NPL)and is essential in discovering the emotional undertones within the text data and, hence, capturingpublic sentiments over a variety of issues. In this regard, this study suggests a deep learningtechnique for sentiment categorization on a Twitter dataset that is based on Long Short-TermMemory (LSTM) networks. Preprocessing is done comprehensively, feature extraction is donethrough a bag of words method, and 80-20 data is split using training and testing. The experimentalfindings demonstrate that the LSTM model outperforms the conventional models, such as SVM andNaïve Bayes, with an F1-score of 99.46%, accuracy of 99.13%, precision of 99.45%, and recall of99.25%. Additionally, AUC-ROC and PR curves validate the model’s effectiveness. Although, itperforms well the model consumes heavy computational resources and longer training time. Insummary, the results show that deep learning performs well in sentiment analysis and can be usedto social media monitoring, customer feedback evaluation, market sentiment analysis, etc.