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
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
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
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Interpreting comparative constructions in biomedical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Just how mad are you? finding strong and weak opinion clauses
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Mining comparative sentences and relations
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
An empirical approach to the interpretation of superlatives
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Toward opinion summarization: linking the sources
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Entity discovery and assignment for opinion mining applications
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Recognizing stances in online debates
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Sentiment analysis of conditional sentences
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Bipolar person name identification of topic documents using principal component analysis
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Aspect-based sentiment analysis of movie reviews on discussion boards
Journal of Information Science
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Liars and saviors in a sentiment annotated corpus of comments to political debates
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Identifying noun product features that imply opinions
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Product comparison using comparative relations
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A model-based EM method for topic person name multi-polarization
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Leveraging relationships in social networks for sentiment analysis
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Annotating preferences in negotiation dialogues
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Identifying comparative claim sentences in full-text scientific articles
ACL '12 Proceedings of the Workshop on Detecting Structure in Scholarly Discourse
Sentiment analysis of sentences with modalities
Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing
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This paper studies sentiment analysis from the user-generated content on the Web. In particular, it focuses on mining opinions from comparative sentences, i.e., to determine which entities in a comparison are preferred by its author. A typical comparative sentence compares two or more entities. For example, the sentence, "the picture quality of Camera X is better than that of Camera Y", compares two entities "Camera X" and "Camera Y" with regard to their picture quality. Clearly, "Camera X" is the preferred entity. Existing research has studied the problem of extracting some key elements in a comparative sentence. However, there is still no study of mining opinions from comparative sentences, i.e., identifying preferred entities of the author. This paper studies this problem, and proposes a technique to solve the problem. Our experiments using comparative sentences from product reviews and forum posts show that the approach is effective.