FPT-ThaiTuan/Using-Word-Embeddings-for-Twitter-Sentiment-Analysis
The project researches sentiment analysis on Twitter, with the goal of evaluating the positivity, negativity or neutrality of comments. Using Word Embeddings, an advanced method in natural language processing, our model achieved a high accuracy of 96.61%. The model was trained on Twitter data and tested on a data comment dataset from Binance.
This project helps you understand public opinion by automatically analyzing Twitter comments. You provide raw Twitter text, and it outputs whether each comment expresses positive, negative, or neutral sentiment. This is ideal for social media managers, market researchers, or brand analysts who need to gauge real-time public perception.
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Use this if you need to quickly classify the sentiment of a large volume of Twitter data.
Not ideal if you need a solution for sentiment analysis on non-Twitter text or require highly nuanced, domain-specific sentiment classifications out-of-the-box.
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Mar 27, 2024
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