Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Sentiment Mining in WebFountain
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 03
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Red Opal: product-feature scoring from reviews
Proceedings of the 8th ACM conference on Electronic commerce
Bootstrapping both Product Properties and Opinion Words from Chinese Reviews with Cross-Training
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Hidden sentiment association in chinese web opinion mining
Proceedings of the 17th international conference on World Wide Web
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Emotion tokens: bridging the gap among multilingual twitter sentiment analysis
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Opinion target extraction using word-based translation model
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Before deciding to buy a product, many people tend to consult others' opinions on it. Web provides a perfect platform which one can get information to find out the advantages and disadvantages of the product of his interest. How to automatically manage the numerous opinionated documents and then to give suggestions to the potential customers is becoming a research hotspot recently. Constructing a sentiment resource is one of the vital elements of opinion finding and polarity analysis tasks. For a specific domain, the sentiment resource can be regarded as a dictionary, which contains a list of product feature words and several opinion words with sentiment polarity for each feature word. This paper proposes an automatic algorithm to extraction feature words and opinion words for the sentiment resource. We mine the feature words and opinion words from the comments on the Web with both NLP technique and statistical method. Left context entropy is proposed to extract unknown feature words; Adjective rules and background corpus are taken into consideration in the algorithm. Experimental results show the effectiveness of the proposed automatic sentiment resource construction approach. The proposed method that combines NLP and statistical techniques is better than using only NLP-based technique. Although the experiment is built on mobile telephone comments in Chinese, the algorithm is domain independent.