Microblog Sentiment Analysis Method Based on Spectral Clustering
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초록
This study evaluates the viewpoints of user focus incidents using microblog sentiment analysis, which hasbeen actively researched in academia. Most existing works have adopted traditional supervised machinelearning methods to analyze emotions in microblogs; however, these approaches may not be suitable inChinese due to linguistic differences. This paper proposes a new microblog sentiment analysis method thatmines associated microblog emotions based on a popular microblog through user-building combined withspectral clustering to analyze microblog content. Experimental results for a public microblog benchmarkcorpus show that the proposed method can improve identification accuracy and save manually labeled timecompared to existing methods.
Abstract
This study evaluates the viewpoints of user focus incidents using microblog sentiment analysis, which hasbeen actively researched in academia. Most existing works have adopted traditional supervised machinelearning methods to analyze emotions in microblogs; however, these approaches may not be suitable inChinese due to linguistic differences. This paper proposes a new microblog sentiment analysis method thatmines associated microblog emotions based on a popular microblog through user-building combined withspectral clustering to analyze microblog content. Experimental results for a public microblog benchmarkcorpus show that the proposed method can improve identification accuracy and save manually labeled timecompared to existing methods.
참고문헌 (20)
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