【目的】結合基于LDA主題識別模型和Ada Boost方法以提高微博文本情感分類準確度。【方法】利用LDA提取微博文本主題分布特征,融合情感特征和句式特征,采用Ada Boost集成分類方法針對上述特征變量訓練情感分類模型。【結果】研究結果表明,主題特征對情感識別有顯著正向作用,基于主題特征和情感特征的模型分類效果最好。借助Ada Boost分類器使得最終情感分類準確率達到84.512%,召回率達到83.160%。【局限】樣本數量有限;情感詞典還不夠完善;同時忽略了微博文本中的表情符號等特征。【結論】本文提出的結合主題分布特征的Ada Boost模型能夠有效地判別用戶情感傾向。

[Objective] The paper aims to improve the performance of sentiment analysis for micro-blog texts with the help of LDA model and AdaBoost algorithm. [Methods] First, we used the LDA topic model to extract topics of micro-blog posts. Then, we merged the emotional and sentence pattern features. Finally, we trained the proposed sentiment analysis model with the AdaBoost ensemble classification method. [Results] The topic feature posed significant positive impacts on emotion recognition therefore, model with topic and emotional features yielded the best results. The precision of the proposed model reached 84.512%, while the recall reached 83.160%. [Limitations] The sample size needs to be expanded, and the sentiment dictionary should be improved too. We did not study the emoticons from the micro-blog posts. [Conclusions] The proposed AdaBoost model with LDA could effectively identify emotional tendencies.