Optimization of Feature-Opinion Pairs in Chinese Customer Reviews
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Chinese Blog Clustering by Hidden Sentiment Factors
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Using morphological and syntactic structures for Chinese opinion analysis
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Aspect-based sentiment analysis of movie reviews on discussion boards
Journal of Information Science
Incorporating Sentiment Prior Knowledge for Weakly Supervised Sentiment Analysis
ACM Transactions on Asian Language Information Processing (TALIP)
Predicting the semantic orientation of terms in E-HowNet
ROCLING '11 Proceedings of the 23rd Conference on Computational Linguistics and Speech Processing
An approach of semi-automatic public sentiment analysis for opinion and district
WAIM'11 Proceedings of the 2011 international conference on Web-Age Information Management
A novel approach for clustering sentiments in Chinese blogs based on graph similarity
Computers & Mathematics with Applications
Demonstration of IlluMe: creating ambient according to instant message logs
ACL '12 Proceedings of the ACL 2012 System Demonstrations
Up or Down? Click-Through Rate Prediction from Social Intention for Search Advertising
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level. © 2007 Wiley Periodicals, Inc.