【目的】通過對電商網站產生的海量用戶評論數據進行綜合分析,及時獲取與產品口碑相關的用戶反饋信息,以便快速有效地反饋企業的市場營銷活動效果。【方法】運用詞袋模型、依存句法分析和機器學習等新興技術,對來自京東和天貓兩個主要電商網站的真實數據集進行分析,實現了電商用戶評論的自動情感分析和觀點標簽提取。【結果】評論情感分析獲得約90%的準確率,利用改進雙向傳播算法成功實現了一個自動化的詞庫構建系統,擺脫對詞典的依賴,該系統的F值達到約71%。【局限】觀點標簽提取的召回率需要進一步提高。【結論】通過實時獲取海量電商評論數據并進行有效分析,成功實現對用戶口碑的快速分析與準確把控,具有較高的商業化推廣前景。

[Objective] This study conducts a comprehensive analysis of huge amount of reviews generated by E-commerce website users, aiming to assess the marketing strategies. [Methods] We used syntactic parsing, bag of words model and machine learning techniques to examine real-world datasets from JD and TMall. The proposed method could analyze sentiment and extract opinion from the reviews automatically. [Results] The accuracy of the sentiment analysis was 90%. We constructed an automatic vocabulary building mechanism without dictionary dependency. The F-measure of the new system was 71%. [Limitations] The recall of the opinion extraction needs to be improved. [Conclusions] The proposed system could effectively monitor the word-of-mouth issues facing products sold online. It could be transferred to many online business.