tailieunhanh - Báo cáo khoa học: "Toward finer-grained sentiment identification in product reviews through linguistic and ontological analyses"

We propose categories of finer-grained polarity for a more effective aspect-based sentiment summary, and describe linguistic and ontological clues that may affect such fine-grained polarity. We argue that relevance for satisfaction, contrastive weight clues, and certain adverbials work to affect the polarity, as evidenced by the statistical analysis. en and P | Toward finer-grained sentiment identification in product reviews through linguistic and ontological analyses Hye-Jin Min Computer Science Department KAIST Daejeon KOREA hjmin@ Jong C. Park Computer Science Department KAIST Daejeon KOREA park@ Abstract We propose categories of finer-grained polarity for a more effective aspect-based sentiment summary and describe linguistic and ontological clues that may affect such fine-grained polarity. We argue that relevance for satisfaction contrastive weight clues and certain adver-bials work to affect the polarity as evidenced by the statistical analysis. 1 Introduction Sentiment analysis have been widely conducted in several domains such as movie reviews product reviews news and blog reviews Pang et al. 2002 Turney 2002 . The unit of the sentiment varies from a document level to a sentence level to a phrase-level where a more fine-grained approach has been receiving more attention for its accuracy. Sentiment analysis on product reviews identifies or summarizes sentiment from reviews by extracting relevant opinions about certain attributes of products such as their parts or properties Hu and Liu 2004 Popescu and Etzioni 2005 . Aspect-based sentiment analysis summarizes sentiments with diverse attributes so that customers may have to look more closely into analyzed sentiments Titov and McDonald 2008 . However there are additional problems. First it is rather hard to choose the right level of detail. If concepts corresponding to attributes are too general the level of detail may not be so much finer than the ones on a document level. On the other hand if concepts are too specific there may be some attributes that are hardly mentioned in the reviews resulting in the data sparseness problem. Second there are cases when some crucial information is lost. For ex ample suppose that two product attributes are mentioned in a sentence with a coordinated or subordinated structure. In this case the .