[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.
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