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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (8): 51-59    DOI: 10.11925/infotech.2096-3467.2018.0060
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Sentiment Analysis for Micro-blogs with LDA and AdaBoost
Ziming Zeng(),Qianwen Yang
Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
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[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.

Key wordsMicro-blog      Sentiment Analysis      LDA      AdaBoost     
Received: 17 January 2018      Published: 08 September 2018

Cite this article:

Ziming Zeng,Qianwen Yang. Sentiment Analysis for Micro-blogs with LDA and AdaBoost. Data Analysis and Knowledge Discovery, 2018, 2(8): 51-59.

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