Word association norms, mutual information, and lexicography
Computational Linguistics
WordNet: a lexical database for English
Communications of the ACM
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Identifying topics by position
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Manual and automatic evaluation of summaries
AS '02 Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Query-Based Summarization of Customer Reviews
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Determining the Polarity and Source of Opinions Expressed in Political Debates
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A method for opinion mining of product reviews using association rules
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
The automatic creation of literature abstracts
IBM Journal of Research and Development
Opinion mining from noisy text data
International Journal on Document Analysis and Recognition - Special Issue NOISY
Ontology Based Opinion Mining for Movie Reviews
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Feature and Opinion Mining for Customer Review Summarization
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Mining popular menu items of a restaurant from web reviews
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
Generating syntactic tree templates for feature-based opinion mining
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Fine-grained Product Features Extraction and Categorization in Reviews Opinion Mining
ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
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The growth of E-commerce has led to the invention of several websites that market and sell products as well as allow users to post reviews. It is typical for an online buyer to refer to these reviews before making a buying decision. Hence, automatic summarization of users' reviews has a great commercial significance. However, since the product reviews are written by nonexperts in an unstructured, natural language text, the task of summarizing them is challenging. This paper presents a semisupervised approach for mining online user reviews to generate comparative feature-based statistical summaries that can guide a user in making an online purchase. It includes various phases like preprocessing and feature extraction and pruning followed by featurebased opinion summarization and overall opinion sentiment classification. Empirical studies indicate that the approach used in the paper can identify opinionated sentences from blog reviews with a high average precision of 91% and can classify the polarity of the reviews with a good average accuracy of 86%.