Valence-Arousal Dimension-based Opinion Mining for Movie Reviews

• Main Authors: Wang, Chen-An, 王晨安
• Other Authors: Hsu, Wen-Lian
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/x75bbw

碩士 === 國立清華大學 === 資訊系統與應用研究所 === 105 === Since Web 2.0 service began, more and more information has been filled with our lives. At the same time, user’s comments and opinions began sharing through social media. Data mining, Machine learning, Natural language processing fields start to focus on semantic and emotion analysis in recent years. In business conduct, these techniques help decision maker predict market trends and discover potential customers. It also involve people’s welfare, medical, climate and other issues. In this study, we extend the dimensional theory of emotion which Russell proposed in 1980. Valence indicates the positive and negative polarity of the word and Arousal indicates the emotion degree of the word. In Valence and Arousal dimension, any Chinese word contains Valence value and Arousal value, both value range are from 1 to 10. We further apply Valence and Arousal dimension to analyze on Chinese movie reviews. To determine whether the user recommend the movie or not, we collected movie reviews from PTT movie forum and processed them with nature language processing approaches. Finally, we use distributed keyword vectors to represent training and testing features. We also compare our method to evaluate its performance with the well-known methods such as LDA-SVM, Naïve Bayes, K-NN, tf-idf and Delta tf-idf in sentiment analysis. The experimental results show our method can achieve the best performance on Valence and Arousal prediction. Also the method can predict unknown word’s Valence value and Arousal value . In opinion mining for movie reviews, our method can consider writer’s emotion polarity and degree. As a result, our method can help us grasp the core of the article accurately and achieve 85.3% accuracy in performance. Keywords：Opinion Mining, Valence&Arousal Dimension, Ontology, Word Embeddings, Movie Review, Sentiment Analysis