Summary of Three-class Text Sentiment Analysis Based on Lstm, by Yin Qixuan
Three-Class Text Sentiment Analysis Based on LSTM
by Yin Qixuan
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a three-class sentiment classification method using Long Short-Term Memory (LSTM) networks to analyze Weibo comments. The LSTM model excels at capturing long-distance dependencies in text data, achieving superior performance compared to traditional machine learning approaches and other deep learning methods. The experimental results demonstrate an accuracy of 98.31% and an F1 score of 98.28%, highlighting the effectiveness of LSTM in capturing nuanced sentiment information within text. Despite its strengths, the model faces challenges such as high computational complexity and slower processing times for lengthy texts. Future work could explore combining pre-trained models or advancing feature engineering techniques to further improve both accuracy and practicality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to figure out what people think of something based on what they say online. It’s called sentiment analysis, and it helps us understand how people feel about things like movies, products, or politicians. The researchers used a special kind of computer model called an LSTM (Long Short-Term Memory) network to analyze comments from the Chinese social media platform Weibo. They found that this model is really good at understanding what people mean when they write something online, and it got better results than other methods. This is important because it can help us understand how people feel about things in real-time, which is useful for businesses and governments. |
Keywords
» Artificial intelligence » Classification » Deep learning » F1 score » Feature engineering » Lstm » Machine learning
