Project Description

This project explores Sentiment Analysis on Social Media Text using a graph-based database (Neo4j). By analyzing tweets obtained via the Twitter API, the sentiment values (positive, negative, or neutral) of users are determined. These sentiments are modeled in a graph database to study user behavior, establish connections, and understand trends in public opinion. Machine Learning techniques, including SVM and LSTM, are employed to classify sentiment and categorize tweets across topics like education, politics, and sports. This analysis can be applied in fields such as advertising, mental health monitoring, and customer sentiment analysis.