意见分析(OpinionMining)-淡江大学.ppt

Terminologies Entity: object Aspect: feature, attribute, facet Opinion holder: opinion source Topic: entity, aspect Product features, political issues * Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, Subjectivity and Emotion Sentence subjectivity An objective sentence presents some factual information, while a subjective sentence expresses some personal feelings, views, emotions, or beliefs. Emotion Emotions are people’s subjective feelings and thoughts. * Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, Emotion Six main emotions Love Joy Surprise Anger Sadness Fear * Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, Abstraction (2): opinion summary With a lot of opinions, a summary is necessary. A multi-document summarization task For factual texts, summarization is to select the most important facts and present them in a sensible order while avoiding repetition 1 fact = any number of the same fact But for opinion documents, it is different because opinions have a quantitative side have targets 1 opinion a number of opinions Aspect-based summary is more suitable Quintuples form the basis for opinion summarization * Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, An aspect-based opinion summary * Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, Visualization of aspect-based summaries of opinions * Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, Visualization of aspect-based summaries of opinions * Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, Classification Based on Supervised Learning Sentimen