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A Supervised Fine-Grained Sentiment Analysis System for Online Reviews



Sentiment analysis, as a heatedly-discussed research topic in the area of information extraction, has attracted more attention from the beginning of this century. With the rapid development of the Internet, especially the rising popularity of Web2.0 technology, network user has become not only the content maker, but also the receiver of information. Meanwhile, benefiting from the development and maturity of the technology in natural language processing and machine learning, we can widely employ sentiment analysis on subjective texts. In this paper, we propose a supervised learning method on fine-grained sentiment analysis to meet the new challenges by exploring new research ideas and methods to further improve the accuracy and practicability of sentiment analysis. First, this paper presents an improved strength computation method of sentiment word. Second, this paper introduces a sentiment information joint recognition model based on Conditional Random Fields and analyzes the related knowledge of the basic and semantic features. Finally, the experimental results show that our approach and a demo system are feasible and effective.



Total Pages: 17
Pages: 589-605


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Volume: 21
Issue: 4
Year: 2015

Cite this document


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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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