【目的】為更精確地識別網民態度,監測網絡輿情,提出一種基于SVM多特征融合的情感5級分類方法。【方法】從詞性特征、情感特征、句式特征、語義特征4個方面,提取動詞、名詞、情感詞、否定詞等14個特征,運用SVM方法對微博情感進行5級分類。【結果】實驗結果表明,該方法對情感5級分類的準確率為82.40%,召回率為81.91%,F值為82.10%。【局限】訓練語料的規模有待進一步提高。【結論】該方法在情感5級分類方面取得較好的效果。

[Objective] This paper proposes a new method based on the Support Vector Machine to monitor online public opinion. [Methods] We extracted fourteen linguistic characteristics of the micro-blog posts and analysed their sentiments with Support Vector Machine. [Results] The precision, recall and F value of the proposed method were 82.40%, 81.91%, and 82.10%, respectively. [Limitations] The size of training corpus needs to be expanded. [Conclusions] The proposed method could effectively analyze sentiments of micro-blog posts.