Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
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
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Determining the semantic orientation of terms through gloss classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
An integration strategy for mining product features and opinions
Proceedings of the 17th ACM conference on Information and knowledge management
Address standardization with latent semantic association
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Research of Product Ranking Technology Based on Opinion Mining
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 04
Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis
ECIR'07 Proceedings of the 29th European conference on IR research
Grouping product features using semi-supervised learning with soft-constraints
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A comparative study of feature selection and machine learning techniques for sentiment analysis
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Competitive intelligence for SMEs: a web-based decision support system
International Journal of Business Information Systems
More than words: Social networks' text mining for consumer brand sentiments
Expert Systems with Applications: An International Journal
Potential Power and Problems in Sentiment Mining of Social Media
International Journal of Strategic Decision Sciences
Introducing spatial context in comparative pricing and product search
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
Text classification using a few labeled examples
Computers in Human Behavior
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The proliferation of blogs and social networks presents a new set of challenges and opportunities in the way information is searched and retrieved. Even though facts still play a very important role when information is sought on a topic, opinions have become increasingly important as well. Opinions expressed in blogs and social networks are playing an important role influencing everything from the products people buy to the presidential candidate they support. Thus, there is a need for a new type of search engine which will not only retrieve facts, but will also enable the retrieval of opinions. Such a search engine can be used in a number of diverse applications like product reviews to aggregating opinions on a political candidate or issue. Enterprises can also use such an engine to determine how users perceive their products and how they stand with respect to competition. This paper presents an algorithm which not only analyzes the overall sentiment of a document/review, but also identifies the semantic orientation of specific components of the review that lead to a particular sentiment. The algorithm is integrated in an opinion search engine which presents results to a query along with their overall tone and a summary of sentiments of the most important features.