## **For better user experience, refer to the Web official document ->  [Sentiment Analysis](https://www.paddlepaddle.org.cn/hublist)**

### Sentiment Analysis

Sentiment Classification (Senta) can automatically determine the sentiment polarity category of Chinese texts with subjective descriptions and give the corresponding confidence level. This can help enterprises understand users' consumption habits, analyze hot topics, and monitor public opinion at crisis, and provide favorable decision support for enterprises.

- Recommended Models

| Model Name                                                   | Model Introduction                                           |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| [Sentiment Analysis - LSTM](https://www.paddlepaddle.org.cn/hubdetail?name=senta_lstm&en_category=SentimentAnalysis) | Implementation of sentiment tendency analysis LSTM           |
| [Sentiment Analysis - GRU](https://www.paddlepaddle.org.cn/hubdetail?name=senta_gru&en_category=SentimentAnalysis) | Implementation of sentiment tendency analysis GRU            |
| [Conversation Sentiment Recognition](https://www.paddlepaddle.org.cn/hubdetail?name=emotion_detection_textcnn&en_category=SentimentAnalysis) | For user texts in an intelligent conversation scene, it automatically determines the sentiment category of the texts and assigns a corresponding confidence level. The sentiment type is classified as positive, negative, or neutral. |
