TWITTER SENTIMENT ANALYSIS

Social media platforms have become powerful sources of real-time information where users continuously
share opinions and emotions. Among them, Twitter stands out due to its fast and concise communication
style, making it ideal for sentiment analysis. This project develops a Twitter Sentiment Analysis System to
classify tweets into positive, negative, and neutral categories using NLP and ML techniques. Data is
collected from APIs and public datasets, which often include noise such as emojis, hashtags, and informal
language. A preprocessing pipeline involving cleaning, tokenization, stop-word removal, and normalization
is applied to improve data quality. Feature extraction methods like TF-IDF and GloVe embeddings convert
text into numerical form. Lexicon-based approaches such as VADER further enhance sentiment detection.
Multiple models including Logistic Regression, SVM, Random Forest, and Bi-LSTM are trained for
classification. An ensemble technique is used to combine model outputs for better accuracy. The system
supports both real-time and batch processing of data. Results show high accuracy and robustness across
domains. This system is useful for business intelligence, brand monitoring, and public opinion analysis.