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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (3): 46-53    DOI: 10.11925/infotech.2096-3467.2017.03.06
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Sentiment Analysis of Trending Topics Based on Relevance
Yue He,Min Xiao(),Yue Zhang
Business School, Sichuan University, Chengdu 610064, China
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[Objective] This paper tries to effectively analyze the sentiment of trending topics with machine learning techniques. [Methods] First, we proposed a new classification model based on trending topic relevance to extract subjective microblog posts. Second, we analyzed sentiment tendency with an improved machine learning method. [Results] We found that the modified model improved the subjective-objective classification of trending topics. The F-measures were increased by 7.4% and 2.2% respectively. [Limitations] More research is needed to study the distribution of data, the particle of emotion and the changes of sentiment trends. [Conclusions] Adding topic relevance factor to the model could improve the performance of sentiment analysis of micro-blog posts, and extract tendency of key objects from the trending topics, which provides intelligence for micro-blog marketing.

Key wordsTrending Topic      Subjective-Objective Classification      Emotion Orientation Classification      TF-IDF-SIM      Machine Learning     
Received: 17 October 2016      Published: 20 April 2017

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Yue He,Min Xiao,Yue Zhang. Sentiment Analysis of Trending Topics Based on Relevance. Data Analysis and Knowledge Discovery, 2017, 1(3): 46-53.

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