A TOOL FOR SENTIMENT CLASSIFICATION IN SOCIAL AND REVIEW DATA

Social media platforms and review sites have experienced explosive user-generated content growth which presents both new opportunities and significant challenges for studying public opinion across large populations. Sentiment analysis through opinion mining uses natural language processing (NLP) and machine learning to automatically identify text as positive, negative or neutral which generates useful insights for businesses and governments and researchers. This paper provides an extensive evaluation of sentiment analysis systems and methods which specifically targets their implementation on social media and review content. The research evaluates lexicon-based methods together with machine learning approaches and hybrid solutions while presenting a step-by-step approach to sentiment classification and assessing tool performance on benchmark datasets and discussing future directions and essential challenges. Our research includes a practical sentiment analysis tool which uses data preprocessing and trained classifiers and deep learning models to enhance accuracy when analyzing informal language and emojis. The tool demonstrates strong performance when tested against real-world Twitter and Amazon review datasets. The research provides original insights together with practical guidance for implementing sentiment analysis in dynamic real-world environments to connect textual data with meaningful emotional intelligence.