| Summary: | 碩士 === 國立臺北科技大學 === 資訊工程系研究所 === 101 === Opinion analysis has grown to be one of the most active research areas in natural language processing. If we can classify reviews and messages of blogs correctly, it will help to analyze product and service competition and to realize the opinion orientations of the people on public issues. Existing research focuses on establishing lexical resources, and utilizing text classification for opinion analysis. Nevertheless, there are some challenges for microblogs. First, text messages are limited to 140 characters. Second, it’s difficult to know what users want to express without suitable contexts; on the other hand, short messages usually contain sentiment words, which could help opinion analysis. In this paper, we propose an opinion orientation estimation approach based on target finding and opinion modifying relations in microblog reviews. First, it collects reviews from microblog and preprocess the source data. Then, by extracting any entity or aspect of the entity about which an opinion has been expressed according to opinion modifying relations, we calculate the overall score of opinion orientation. Finally, by utilizing the opinion orientation in microblog reviews for recommendation, the cold start problem can be overcome.
In our experiment on the 1000 movie reviews of 50 movies from Twitter, the average accuracy of the proposed method is 84.44%, and the highest precision is 88.89%, which is better than SVM and Naive Bayes. This validates the higher precision from modifying relation identification for opinion orientation classification.
|
|---|
