An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

@article{pang2008opinion, author = {Pang, Bo and Lee, Lillian}, title = {Opinion Mining and Sentiment Analysis}, journal = {Foundations and Trends in Information Retrieval}, publisher = {Now Publishers Inc.}, address = {Hanover, MA, USA}, year = {2008}, volume = {2}, number = {1-2}, pages = {1--135}, url = {http://portal.acm.org/citation.cfm?id=1454712}, doi = {10.1561/1500000011}, issn = {1554-0669}, keywords = {sentiment, social, analysis, opinion, mining, web}, abstract = {An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.} }

%0 = article %A = Pang, Bo and Lee, Lillian %C = Hanover, MA, USA %D = 2008 %I = Now Publishers Inc. %T = Opinion Mining and Sentiment Analysis %U = http://portal.acm.org/citation.cfm?id=1454712

@article{Pang2008, author = {Pang, Bo and Lee, Lillian}, title = {Opinion mining and sentiment analysis}, journal = {Foundations and Trends® in Information Retrieval}, year = {2008}, volume = {2}, number = {1-2}, pages = {1-135}, url = {http://www.cs.cornell.edu/home/llee/omsa/omsa-published.pdf}, isbn = {978-1-60198-150-9}, keywords = {sentiment, analysis, opinion, mining, toread} }

%0 = article %A = Pang, Bo and Lee, Lillian %D = 2008 %T = Opinion mining and sentiment analysis %U = http://www.cs.cornell.edu/home/llee/omsa/omsa-published.pdf

@inproceedings{DBLP:conf/acl/PangL04, author = {Pang, Bo and Lee, Lillian}, title = {A Sentimental Education: Sentiment Analysis Using Subjectivity
Summarization Based on Minimum Cuts}, editor = {Scott, Donia and Daelemans, Walter and Walker, Marilyn A.}, booktitle = {ACL}, publisher = {ACL}, year = {2004}, pages = {271--278}, keywords = {2014, kde, bachelorarbeit} }

%0 = inproceedings %A = Pang, Bo and Lee, Lillian %B = ACL %D = 2004 %I = ACL %T = A Sentimental Education: Sentiment Analysis Using Subjectivity
Summarization Based on Minimum Cuts

@inproceedings{Pang+Lee+Vaithyanathan:02a, author = {Pang, Bo and Lee, Lillian and Vaithyanathan, Shivakumar}, title = {Thumbs Up?: Sentiment Classification Using Machine Learning Techniques}, booktitle = {Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10}, series = {EMNLP '02}, publisher = {Association for Computational Linguistics}, address = {Stroudsburg, PA, USA}, year = {2002}, pages = {79--86}, url = {http://dx.doi.org/10.3115/1118693.1118704}, doi = {10.3115/1118693.1118704}, keywords = {2014, kde, bachelorarbeit} }

%0 = inproceedings %A = Pang, Bo and Lee, Lillian and Vaithyanathan, Shivakumar %B = Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10 %C = Stroudsburg, PA, USA %D = 2002 %I = Association for Computational Linguistics %T = Thumbs Up?: Sentiment Classification Using Machine Learning Techniques %U = http://dx.doi.org/10.3115/1118693.1118704

@inproceedings{1034693, author = {Lee, Lillian}, title = {Measures of distributional similarity}, booktitle = {Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics}, publisher = {Association for Computational Linguistics}, address = {Morristown, NJ, USA}, year = {1999}, pages = {25--32}, url = {http://portal.acm.org/citation.cfm?id=1034693&dl=}, doi = {http://dx.doi.org/10.3115/1034678.1034693}, isbn = {1-55860-609-3}, keywords = {similarity, measure, toread} }

%0 = inproceedings %A = Lee, Lillian %B = Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics %C = Morristown, NJ, USA %D = 1999 %I = Association for Computational Linguistics %T = Measures of distributional similarity %U = http://portal.acm.org/citation.cfm?id=1034693&dl=