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| Extracting Topic and Opinion from Microblog Posts with New Algorithm |
Yao Zhaoxu( ),Ma Jing |
| College of Economic and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China |
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Abstract [Objective] This paper proposes an algorithm to extract topic and opinion information from the microblog posts automatically. [Methods] First, we used the improved TF-IDF algorithm to build the topic characteristic word vector. Second, we generated lexical chain for the topics based on the relevance among words of the vector. Finally, we extracted the topic and opinion information with the sentiment dictionary, and then generated the “topic+opinion” entries. [Results] We analyzed 24,598 Sina microblog posts of four trending events from June 2014 to June 2015 retrieved by a specially designed crawler. The precision and recall rates of the proposed method were 80.3% and 76.67%, respectively. [Limitations] The data size was small, the effect that the topic model extracted the feature about Weibo still required to be improved. [Conclusions] The proposed algorithm could effectively extract the “topic and opinion” information from micoblog posts.
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Received: 28 January 2016
Published: 29 September 2016
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