【目的】從大量用戶評論中分析用戶偏好,發現產品或服務的不足并提供改進依據。【方法】選取大眾點評網有關餐飲業的用戶評論數據,對大量無監督語料進行預訓練;用少量的標簽數據微調預訓練語言模型;對產品評論中各屬性進行情感得分量化,并結合KANO模型分析用戶對產品或服務的偏好。【結果】將餐飲業用戶的產品評論數據轉化為用戶對產品或服務的偏好。【局限】運用KANO模型時,默認將所有用戶對產品某屬性的偏好視為一致,導致整體偏好分析不準確。【結論】采用PreLM-FT細粒度情感分析,能夠在僅有少量標簽數據的情境下,將用戶評論數據轉化為用戶偏好得分。

[Objective] This paper identifies user preferences based on their reviews of the catering providers,aiming to find and improve the un-satisfactory products or services. [Methods] Firstly, we retrieved user reviews on catering industry from the DianPing website to pre-train unsupervised corpus. Then, we fine-tuned the pretraining language model with a small amount of label data. Finally, we quantified the sentiment scores of attributes from user reviews and combined the KANO model to analyze their preferences for products or services.[Results] We successfully identified user preferences with their reviews. [Limitations] The KANO model might yield some inaccurate overall preference analysis. [Conclusions] The proposed method could effectively reveal user preferences with the help of reviews and some label data.