Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models
Quick Answer
This study evaluates sentiment analysis on Twitter using various models, highlighting LSTM's superior performance with a training accuracy of 90.98% and testing accuracy of 80.00%.
Quick Take
The research demonstrates that LSTM outperforms traditional methods like logistic regression and random forest in capturing contextual nuances.
Key Points
- LSTM achieved a training accuracy of 90.98% and testing accuracy of 80.00%.
- The study compares LSTM with logistic regression, random forest, and naive Bayes.
- LSTM outperformed traditional models in sentiment classification tasks.
- Sentiment analysis helps interpret public opinion and forecast trends.
- The research utilized a preprocessed Kaggle Twitter dataset for evaluation.
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— Originally published at arxiv.org
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