Sentiment Scope Analyzer
| Title of the Project |
Sentiment Scope Analyzer |
| Students Details |
201911277 Merar Jalaleddin |
| Abstract |
IThe rapid growth of social media and other online platforms has led to a surge in the availability of textual data, making sentiment analysis increasingly crucial for understanding public opinion on various topics. This report presents Sentiment Scope Analysis (SSA), a web application developed to enable efficient, real-time sentiment analysis of Twitter data, text inputs, and Excel files. Using the Python-based TextBlob library for Natural Language Processing (NLP), SSA provides a user-friendly interface to analyze sentiment, making data-driven insights accessible to users without a background in data analysis. The application supports secure user authentication, efficient data storage using SQLite, and robust data retrieval functionality. Challenges encountered during development, such as changes to Twitter's scraping policies, and their respective solutions are discussed. The report concludes with an evaluation of SSA's performance, user feedback, and potential future enhancements to improve data visualization, add more data sources, and extend analysis features. |