1> Query classification was the subject of KDD Cup 2005. TREC 2006
blog track dealt with whether texts were opinionated or not; and if
they were, which portions were opinionated.
2> Section 1.4 is important for finding further papers and related work.
3> "Opinion mining" and "Sentiment analysis" are comparable (though not fully equivalent) terms; while the former was more popular with WWW community, the latter was more popular with ACL (NLP) community.
4> Similarity with spam detection. Humans are less apt at tagging sentiments/opinions correctly.
5> Binary feature vectors are more influential in opinion mining than numerical (e.g., tfidf type) feature vectors.
6> Markedly different from topic modeling or IR. Hapax legomena are extremely important here, while repeated occurrence of a word (or a group of words) is not.
7> Topic-based summarization vs opinion-based summarization: In the former case, the first few sentences of a document are generally best summarizers. In the latter case, the last few sentences of a document were found to be the best summarizers.
8> One study has found that there is a real economic effect to be observed when factoring in reviewer credibility: Gu et al. [114] note that a weighted average of message-board postings in which poster credibility is factored in has “prediction power over future abnormal returns of the stock”, but if postings are weighted uniformly, the predictive power disappears.
Note the similar finding in Sumit Bhatia's work on online threads. There are information seekers (equivalent to low quality reviewers) and information providers (equivalent to high quality reviewers).
Triggered research questions:
1> Can reinforcement learning be used for opinion mining/sentiment analysis? Are there algorithms of this kind? What I allude to is that as more and more data comes in, it might be possible to "refine" our earlier opinions in some way - which is a traditional reinforcement learning tactic.
Note that it is also much like a simulated annealing/genetic algorithm/evolutionary computation type thing. Are there algorithms that employ these to solve opinion mining/sentiment analysis problems? Search in the book.
Also note "prior polarity" and "contextual polarity" in 320.
2> Are there online/streaming algorithms for opinion mining?
3> HCI-type evaluation of the graphical summaries ("graphical summary interface")
4> Reviewing the reviews: review quality assessment - can formality be used/introduced as a user-review-quality-independent measure? Read 19, 99, 161, 329, 106, 193, 262, 161 for previous work.
We can also pose it as an exploratory analysis paper: "Formality of product reviews" or "Information content of product reviews". Has there been prior work along these lines? Check. 106, 107, 161 and 329 are already worth looking at. Also check section 5.2.4.2 (and all its cited papers) for the research question "formality and reviewer-credibility".
Note that our "implicature score" may also come in handy for measuring review quality.
5> A new feature - "bag of POS". Check prior work, if any.
6> How about jointly plotting and/or modeling the temporal trend of sales and opinions? Any prior work?
Reported corpora:
Blog06, BlogPulse, Congressional floor debate transcripts, Cornell movie review datasets, customer review datasets, Economining, French sentences, MPQA corpus, multiple-aspect restaurant reviews, multi-domain sentiment dataset, NTCIR multilingual corpus, review-search result sets, OpQA corpus
Reported lexica:
General inquirer, OpinionFinder’s Subjectivity Lexicon, SentiWordnet, Taboada and Grieve’s Turney adjective list
More labels/tags for this post:
winner circle bias, sock puppet, sock puppetry, hedonic regression
Bibtex entry:
@article{Pang+Lee:08b,
author = {Bo Pang and Lillian Lee},
title = {Opinion mining and sentiment analysis},
journal = {Foundations and Trends in Information Retrieval},
year = {2008},
volume = {2},
number = {1-2},
pages = {1--135}
}
Interesting papers for further reading:
1> Learning to laugh (automatically): Computational models for humor recognition
2> Word Sense and Subjectivity by Wiebe and Mihalcea
3> Learning Subjective Language by Wiebe, et al
4> Thumbs up? Sentiment Classification using Machine Learning Techniques
5> A. Anagnostopoulos, A. Z. Broder, and D. Carmel, “Sampling search-engine results,” World Wide Web, vol. 9, pp. 397–429, 2006.
6> Feature engineering for text classification
7a> Isotonic Conditional Random Fields and Local Sentiment Flow
7b> Generalized Isotonic Conditional Random Fields
8> Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 347–354, 2005.
9> Theresa Wilson, Janyce Wiebe, and Rebecca Hwa. Just how mad are you? Finding strong and weak opinion clauses. In Proceedings of AAAI, pages 761–769, 2004. Extended version in Computational Intelligence 22(2, Special Issue on Sentiment Analysis):73–99, 2006.
10> Fabrizio Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1–47, 2002.
11> Vasileios Hatzivassiloglou and Janyce Wiebe. Effects of adjective orientation and gradability on sentence subjectivity. In Proceedings of the International Conference on Computational Linguistics (COLING), 2000.
12> Peter Turney. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the Association for Computational Linguistics (ACL), pages 417–424, 2002.
13> Hong Yu and Vasileios Hatzivassiloglou. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2003.
14> Ann Devitt and Khurshid Ahmad. Sentiment analysis in financial news: A cohesion-based approach. In Proceedings of the Association for Computational Linguistics (ACL), pages 984–991, 2007.
15> Koji Eguchi and Victor Lavrenko. Sentiment retrieval using generative models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 345–354, 2006.
16> Wei-Hao Lin and Alexander Hauptmann. Are these documents written from different perspectives? A test of different perspectives based on statistical distribution divergence. In Proceedings of the International Conference on Computational Linguistics (COLING)/Proceedings of the Association for Computational Linguistics (ACL), pages 1057–1064, Sydney, Australia, July 2006. Association for Computational Linguistics.
17> Claire Cardie, JanyceWiebe, TheresaWilson, and Diane Litman. Combining low-level and summary representations of opinions for multi-perspective question answering. In Proceedings of the AAAI Spring Symposium on New Directions in Question Answering, pages 20–27, 2003.
18> Giuseppe Carenini, Raymond Ng, and Adam Pauls. Multi-document summarization of evaluative text. In Proceedings of the European Chapter of the Association for Computational Linguistics (EACL), pages 305–312, 2006.
19> Claire Cardie. Empirical methods in information extraction. AI Magazine, 18(4):65–79, 1997.
20> Luca Dini and Giampaolo Mazzini. Opinion classification through information extraction. In Proceedings of the Conference on Data Mining Methods and Databases for Engineering, Finance and Other Fields (Data Mining), pages 299–310, 2002.
21> Judee K. Burgoon, J. P. Blair, Tiantian Qin, and Jay F. Nunamaker, Jr. Detecting deception through linguistic analysis. In Proceedings of Intelligence and Security Informatics (ISI), number 2665 in Lecture Notes in Computer Science, page 958, 2008.
22> Veselin Stoyanov, Claire Cardie, Diane Litman, and Janyce Wiebe. Evaluating an opinion annotation scheme using a new multi-perspective question and answer corpus. In Qu et al. [245]. AAAI technical report SS-04-07.
23> Janyce Wiebe, Theresa Wilson, and Claire Cardie. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation (formerly Computers and the Humanities), 39(2/3): 164–210, 2005.
24> Janyce M. Wiebe, Rebecca F. Bruce, and Thomas P. O’Hara. Development and use of a gold standard data set for subjectivity classifications. In Proceedings of the Association for Computational Linguistics (ACL), pages 246–253, 1999.
25a> Nitin Jindal and Bing Liu. Identifying comparative sentences in text documents. In Proceedings of the ACM Special Interest Group on Information Retrieval (SIGIR), 2006. [This is the longer and more comprehensive version.]
25b> Nitin Jindal and Bing Liu. Mining comparative sentences and relations. In Proceedings of AAAI, 2006.
26> Eric Breck and Claire Cardie. Playing the telephone game: Determining the hierarchical structure of perspective and speech expressions. In Proceedings of the International Conference on Computational Linguistics (COLING), 2004.
27> Jeonghee Yi, Tetsuya Nasukawa, Razvan Bunescu, and Wayne Niblack. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2003.
28> Minqing Hu and Bing Liu. Mining opinion features in customer reviews. In Proceedings of AAAI, pages 755–760, 2004.
29> Christian Jacquemin. Spotting and Discovering Terms through Natural Language Processing. MIT Press, 2001.
30> Rayid Ghani, Katharina Probst, Yan Liu, Marko Krema, and Andrew Fano. Text mining for product attribute extraction. SIGKDD Explorations Newsletter, 8(1):41–48, 2006.
31> Ana-Maria Popescu and Oren Etzioni. Extracting product features and opinions from reviews. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), 2005.
32> Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, and Toshikazu Fukushima. Mining product reputations on the web. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 341–349, 2002. Industry track.
33> Tony Mullen and Robert Malouf. A preliminary investigation into sentiment analysis of informal political discourse. In AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAICAAW), pages 159–162, 2006.
34> Tony Mullen and Robert Malouf. Taking sides: User classification for informal online political discourse. Internet Research, 18:177–190, 2008.
35> Yejin Choi, Claire Cardie, Ellen Riloff, and Siddharth Patwardhan. Identifying sources of opinions with conditional random fields and extraction patterns. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), 2005.
36> Steven Bethard, Hong Yu, Ashley Thornton, Vasileios Hatzivassiloglou, and Dan Jurafsky. Automatic extraction of opinion propositions and their holders. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text, 2004.
37> Yejin Choi, Eric Breck, and Claire Cardie. Joint extraction of entities and relations for opinion recognition. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2006.
38> Dan Roth and Wen Yih. Probabilistic reasoning for entity and relation recognition. In Proceedings of the International Conference on Computational Linguistics (COLING), 2004.
39> Soo-Min Kim and Eduard Hovy. Identifying and analyzing judgment opinions. In Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL), 2006.
40> Veselin Stoyanov and Claire Cardie. Partially supervised coreference resolution for opinion summarization through structured rule learning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 336–344, Sydney, Australia, July 2006. Association for Computational Linguistics.
41> Bo Pang and Lillian Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the Association for Computational Linguistics (ACL), pages 271–278, 2004.
42> Shaikh Mostafa Al Masum, Helmut Prendinger, and Mitsuru Ishizuka. SenseNet: A linguistic tool to visualize numerical-valence based sentiment of textual data. In Proceedings of the International Conference on Natural Language Processing (ICON), pages 147–152, 2007. Poster.
43a> Michael White, Claire Cardie, and Vincent Ng. Detecting discrepancies in numeric estimates using multidocument hypertext summaries. In Proceedings of the Conference on Human Language Technology, pages 336–341, 2002.
43b> Michael White, Claire Cardie, Vincent Ng, KiriWagstaff, and Daryl McCullough. Detecting discrepancies and improving intelligibility: Two preliminary evaluations of RIPTIDES. In Proceedings of the Document Understanding Conference (DUC), 2001.
44> Ehud Reiter and Robert Dale. Building Natural Language Generation Systems. Cambridge, 2000.
45> Lun-Wei Ku, Yu-Ting Liang, and Hsin-Hsi Chen. Opinion extraction, summarization and tracking in news and blog corpora. In AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), pages 100–107, 2006.
46> All LREC papers
47> Yukiko Kawai, Tadahiko Kumamoto, and Katsumi Tanaka. Fair News Reader: Recommending news articles with different sentiments based on user preference. In Proceedings of Knowledge-Based Intelligent Information and Engineering Systems (KES), number 4692 in Lecture Notes in Computer Science, pages 612–622, 2007.
48> Xiaodan Song, Yun Chi, Koji Hino, and Belle Tseng. Identifying opinion leaders in the blogosphere. In Proceedings of the ACM SIGIR Conference on Information and Knowledge Management (CIKM), pages 971–974, 2007.
49> Meishan Hu, Aixin Sun, and Ee-Peng Lim. Comments-oriented blog summarization by sentence extraction. In Proceedings of the ACM SIGIR Conference on Information and Knowledge Management (CIKM), pages 901–904, 2007. ISBN 978-1-59593-803-9. Poster paper.
50> Stephen Wan and Kathy McKeown. Generating overview summaries of ongoing email thread discussions. In Proceedings of the International Conference on Computational Linguistics (COLING), pages 549–555, Geneva, Switzerland, 2004.
51> Liang Zhou and Eduard Hovy. On the summarization of dynamically introduced information: Online discussions and blogs. In AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), pages 237–242, 2006.
52> Minqing Hu and Bing Liu. Mining and summarizing customer reviews. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 168–177, 2004.
53> Li Zhuang, Feng Jing, Xiao-yan Zhu, and Lei Zhang. Movie review mining and summarization. In Proceedings of the ACM SIGIR Conference on Information and Knowledge Management (CIKM), 2006.
54> Nan Hu, Paul A. Pavlou, and Jennifer Zhang. Can online reviews reveal a product’s true quality?: empirical findings and analytical modeling of online word-of-mouth communication. In Proceedings of Electronic Commerce (EC), pages 324–330, New York, NY, USA, 2006. ACM.
55> Lu´ıs Cabral and Ali Hortac¸su. The dynamics of seller reputation: Theory and evidence from eBay. Working paper, downloaded version revised in March, 2006. URL http://pages.stern.nyu.edu/˜lcabral/workingpapers/CabralHortacsu_Mar06.pdf.
56> Pero Subasic and Alison Huettner. Affect analysis of text using fuzzy semantic typing. IEEE Transactions on Fuzzy Systems, 9(4):483–496, 2001.
57> James Allan. Introduction to topic detection and tracking. In James Allan, editor, Topic detection and tracking: Event-based information organization, pages 1–16, Norwell, MA, USA, 2002. Kluwer Academic Publishers. ISBN 0-7923-7664-1.
58> Gilad Mishne and Maarten de Rijke. Moodviews: Tools for blog mood analysis. In AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), pages 153–154, 2006.
59> Baruch Awerbuch and Robert Kleinberg. Competitive collaborative learning. In Proceedings of the Conference on Learning Theory (COLT), pages 233–248, 2005. Journal version to appear in Journal of Computer and System Sciences, special issue on computational learning theory.
60> Benjamin Snyder and Regina Barzilay. Multiple aspect ranking using the Good Grief algorithm. In Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL), pages 300–307, 2007.
61> Andrea Esuli and Fabrizio Sebastiani. Determining term subjectivity and term orientation for opinion mining. In Proceedings of the European Chapter of the Association for Computational Linguistics (EACL), 2006.
2> Section 1.4 is important for finding further papers and related work.
3> "Opinion mining" and "Sentiment analysis" are comparable (though not fully equivalent) terms; while the former was more popular with WWW community, the latter was more popular with ACL (NLP) community.
4> Similarity with spam detection. Humans are less apt at tagging sentiments/opinions correctly.
5> Binary feature vectors are more influential in opinion mining than numerical (e.g., tfidf type) feature vectors.
6> Markedly different from topic modeling or IR. Hapax legomena are extremely important here, while repeated occurrence of a word (or a group of words) is not.
7> Topic-based summarization vs opinion-based summarization: In the former case, the first few sentences of a document are generally best summarizers. In the latter case, the last few sentences of a document were found to be the best summarizers.
8> One study has found that there is a real economic effect to be observed when factoring in reviewer credibility: Gu et al. [114] note that a weighted average of message-board postings in which poster credibility is factored in has “prediction power over future abnormal returns of the stock”, but if postings are weighted uniformly, the predictive power disappears.
Note the similar finding in Sumit Bhatia's work on online threads. There are information seekers (equivalent to low quality reviewers) and information providers (equivalent to high quality reviewers).
Triggered research questions:
1> Can reinforcement learning be used for opinion mining/sentiment analysis? Are there algorithms of this kind? What I allude to is that as more and more data comes in, it might be possible to "refine" our earlier opinions in some way - which is a traditional reinforcement learning tactic.
Note that it is also much like a simulated annealing/genetic algorithm/evolutionary computation type thing. Are there algorithms that employ these to solve opinion mining/sentiment analysis problems? Search in the book.
Also note "prior polarity" and "contextual polarity" in 320.
2> Are there online/streaming algorithms for opinion mining?
3> HCI-type evaluation of the graphical summaries ("graphical summary interface")
4> Reviewing the reviews: review quality assessment - can formality be used/introduced as a user-review-quality-independent measure? Read 19, 99, 161, 329, 106, 193, 262, 161 for previous work.
We can also pose it as an exploratory analysis paper: "Formality of product reviews" or "Information content of product reviews". Has there been prior work along these lines? Check. 106, 107, 161 and 329 are already worth looking at. Also check section 5.2.4.2 (and all its cited papers) for the research question "formality and reviewer-credibility".
Note that our "implicature score" may also come in handy for measuring review quality.
5> A new feature - "bag of POS". Check prior work, if any.
6> How about jointly plotting and/or modeling the temporal trend of sales and opinions? Any prior work?
Reported corpora:
Blog06, BlogPulse, Congressional floor debate transcripts, Cornell movie review datasets, customer review datasets, Economining, French sentences, MPQA corpus, multiple-aspect restaurant reviews, multi-domain sentiment dataset, NTCIR multilingual corpus, review-search result sets, OpQA corpus
Reported lexica:
General inquirer, OpinionFinder’s Subjectivity Lexicon, SentiWordnet, Taboada and Grieve’s Turney adjective list
More labels/tags for this post:
winner circle bias, sock puppet, sock puppetry, hedonic regression
Bibtex entry:
@article{Pang+Lee:08b,
author = {Bo Pang and Lillian Lee},
title = {Opinion mining and sentiment analysis},
journal = {Foundations and Trends in Information Retrieval},
year = {2008},
volume = {2},
number = {1-2},
pages = {1--135}
}
Interesting papers for further reading:
1> Learning to laugh (automatically): Computational models for humor recognition
2> Word Sense and Subjectivity by Wiebe and Mihalcea
3> Learning Subjective Language by Wiebe, et al
4> Thumbs up? Sentiment Classification using Machine Learning Techniques
5> A. Anagnostopoulos, A. Z. Broder, and D. Carmel, “Sampling search-engine results,” World Wide Web, vol. 9, pp. 397–429, 2006.
6> Feature engineering for text classification
7a> Isotonic Conditional Random Fields and Local Sentiment Flow
7b> Generalized Isotonic Conditional Random Fields
8> Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 347–354, 2005.
9> Theresa Wilson, Janyce Wiebe, and Rebecca Hwa. Just how mad are you? Finding strong and weak opinion clauses. In Proceedings of AAAI, pages 761–769, 2004. Extended version in Computational Intelligence 22(2, Special Issue on Sentiment Analysis):73–99, 2006.
10> Fabrizio Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1–47, 2002.
11> Vasileios Hatzivassiloglou and Janyce Wiebe. Effects of adjective orientation and gradability on sentence subjectivity. In Proceedings of the International Conference on Computational Linguistics (COLING), 2000.
12> Peter Turney. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the Association for Computational Linguistics (ACL), pages 417–424, 2002.
13> Hong Yu and Vasileios Hatzivassiloglou. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2003.
14> Ann Devitt and Khurshid Ahmad. Sentiment analysis in financial news: A cohesion-based approach. In Proceedings of the Association for Computational Linguistics (ACL), pages 984–991, 2007.
15> Koji Eguchi and Victor Lavrenko. Sentiment retrieval using generative models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 345–354, 2006.
16> Wei-Hao Lin and Alexander Hauptmann. Are these documents written from different perspectives? A test of different perspectives based on statistical distribution divergence. In Proceedings of the International Conference on Computational Linguistics (COLING)/Proceedings of the Association for Computational Linguistics (ACL), pages 1057–1064, Sydney, Australia, July 2006. Association for Computational Linguistics.
17> Claire Cardie, JanyceWiebe, TheresaWilson, and Diane Litman. Combining low-level and summary representations of opinions for multi-perspective question answering. In Proceedings of the AAAI Spring Symposium on New Directions in Question Answering, pages 20–27, 2003.
18> Giuseppe Carenini, Raymond Ng, and Adam Pauls. Multi-document summarization of evaluative text. In Proceedings of the European Chapter of the Association for Computational Linguistics (EACL), pages 305–312, 2006.
19> Claire Cardie. Empirical methods in information extraction. AI Magazine, 18(4):65–79, 1997.
20> Luca Dini and Giampaolo Mazzini. Opinion classification through information extraction. In Proceedings of the Conference on Data Mining Methods and Databases for Engineering, Finance and Other Fields (Data Mining), pages 299–310, 2002.
21> Judee K. Burgoon, J. P. Blair, Tiantian Qin, and Jay F. Nunamaker, Jr. Detecting deception through linguistic analysis. In Proceedings of Intelligence and Security Informatics (ISI), number 2665 in Lecture Notes in Computer Science, page 958, 2008.
22> Veselin Stoyanov, Claire Cardie, Diane Litman, and Janyce Wiebe. Evaluating an opinion annotation scheme using a new multi-perspective question and answer corpus. In Qu et al. [245]. AAAI technical report SS-04-07.
23> Janyce Wiebe, Theresa Wilson, and Claire Cardie. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation (formerly Computers and the Humanities), 39(2/3): 164–210, 2005.
24> Janyce M. Wiebe, Rebecca F. Bruce, and Thomas P. O’Hara. Development and use of a gold standard data set for subjectivity classifications. In Proceedings of the Association for Computational Linguistics (ACL), pages 246–253, 1999.
25a> Nitin Jindal and Bing Liu. Identifying comparative sentences in text documents. In Proceedings of the ACM Special Interest Group on Information Retrieval (SIGIR), 2006. [This is the longer and more comprehensive version.]
25b> Nitin Jindal and Bing Liu. Mining comparative sentences and relations. In Proceedings of AAAI, 2006.
26> Eric Breck and Claire Cardie. Playing the telephone game: Determining the hierarchical structure of perspective and speech expressions. In Proceedings of the International Conference on Computational Linguistics (COLING), 2004.
27> Jeonghee Yi, Tetsuya Nasukawa, Razvan Bunescu, and Wayne Niblack. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2003.
28> Minqing Hu and Bing Liu. Mining opinion features in customer reviews. In Proceedings of AAAI, pages 755–760, 2004.
29> Christian Jacquemin. Spotting and Discovering Terms through Natural Language Processing. MIT Press, 2001.
30> Rayid Ghani, Katharina Probst, Yan Liu, Marko Krema, and Andrew Fano. Text mining for product attribute extraction. SIGKDD Explorations Newsletter, 8(1):41–48, 2006.
31> Ana-Maria Popescu and Oren Etzioni. Extracting product features and opinions from reviews. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), 2005.
32> Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, and Toshikazu Fukushima. Mining product reputations on the web. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 341–349, 2002. Industry track.
33> Tony Mullen and Robert Malouf. A preliminary investigation into sentiment analysis of informal political discourse. In AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAICAAW), pages 159–162, 2006.
34> Tony Mullen and Robert Malouf. Taking sides: User classification for informal online political discourse. Internet Research, 18:177–190, 2008.
35> Yejin Choi, Claire Cardie, Ellen Riloff, and Siddharth Patwardhan. Identifying sources of opinions with conditional random fields and extraction patterns. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), 2005.
36> Steven Bethard, Hong Yu, Ashley Thornton, Vasileios Hatzivassiloglou, and Dan Jurafsky. Automatic extraction of opinion propositions and their holders. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text, 2004.
37> Yejin Choi, Eric Breck, and Claire Cardie. Joint extraction of entities and relations for opinion recognition. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2006.
38> Dan Roth and Wen Yih. Probabilistic reasoning for entity and relation recognition. In Proceedings of the International Conference on Computational Linguistics (COLING), 2004.
39> Soo-Min Kim and Eduard Hovy. Identifying and analyzing judgment opinions. In Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL), 2006.
40> Veselin Stoyanov and Claire Cardie. Partially supervised coreference resolution for opinion summarization through structured rule learning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 336–344, Sydney, Australia, July 2006. Association for Computational Linguistics.
41> Bo Pang and Lillian Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the Association for Computational Linguistics (ACL), pages 271–278, 2004.
42> Shaikh Mostafa Al Masum, Helmut Prendinger, and Mitsuru Ishizuka. SenseNet: A linguistic tool to visualize numerical-valence based sentiment of textual data. In Proceedings of the International Conference on Natural Language Processing (ICON), pages 147–152, 2007. Poster.
43a> Michael White, Claire Cardie, and Vincent Ng. Detecting discrepancies in numeric estimates using multidocument hypertext summaries. In Proceedings of the Conference on Human Language Technology, pages 336–341, 2002.
43b> Michael White, Claire Cardie, Vincent Ng, KiriWagstaff, and Daryl McCullough. Detecting discrepancies and improving intelligibility: Two preliminary evaluations of RIPTIDES. In Proceedings of the Document Understanding Conference (DUC), 2001.
44> Ehud Reiter and Robert Dale. Building Natural Language Generation Systems. Cambridge, 2000.
45> Lun-Wei Ku, Yu-Ting Liang, and Hsin-Hsi Chen. Opinion extraction, summarization and tracking in news and blog corpora. In AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), pages 100–107, 2006.
46> All LREC papers
47> Yukiko Kawai, Tadahiko Kumamoto, and Katsumi Tanaka. Fair News Reader: Recommending news articles with different sentiments based on user preference. In Proceedings of Knowledge-Based Intelligent Information and Engineering Systems (KES), number 4692 in Lecture Notes in Computer Science, pages 612–622, 2007.
48> Xiaodan Song, Yun Chi, Koji Hino, and Belle Tseng. Identifying opinion leaders in the blogosphere. In Proceedings of the ACM SIGIR Conference on Information and Knowledge Management (CIKM), pages 971–974, 2007.
49> Meishan Hu, Aixin Sun, and Ee-Peng Lim. Comments-oriented blog summarization by sentence extraction. In Proceedings of the ACM SIGIR Conference on Information and Knowledge Management (CIKM), pages 901–904, 2007. ISBN 978-1-59593-803-9. Poster paper.
50> Stephen Wan and Kathy McKeown. Generating overview summaries of ongoing email thread discussions. In Proceedings of the International Conference on Computational Linguistics (COLING), pages 549–555, Geneva, Switzerland, 2004.
51> Liang Zhou and Eduard Hovy. On the summarization of dynamically introduced information: Online discussions and blogs. In AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), pages 237–242, 2006.
52> Minqing Hu and Bing Liu. Mining and summarizing customer reviews. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 168–177, 2004.
53> Li Zhuang, Feng Jing, Xiao-yan Zhu, and Lei Zhang. Movie review mining and summarization. In Proceedings of the ACM SIGIR Conference on Information and Knowledge Management (CIKM), 2006.
54> Nan Hu, Paul A. Pavlou, and Jennifer Zhang. Can online reviews reveal a product’s true quality?: empirical findings and analytical modeling of online word-of-mouth communication. In Proceedings of Electronic Commerce (EC), pages 324–330, New York, NY, USA, 2006. ACM.
55> Lu´ıs Cabral and Ali Hortac¸su. The dynamics of seller reputation: Theory and evidence from eBay. Working paper, downloaded version revised in March, 2006. URL http://pages.stern.nyu.edu/˜lcabral/workingpapers/CabralHortacsu_Mar06.pdf.
56> Pero Subasic and Alison Huettner. Affect analysis of text using fuzzy semantic typing. IEEE Transactions on Fuzzy Systems, 9(4):483–496, 2001.
57> James Allan. Introduction to topic detection and tracking. In James Allan, editor, Topic detection and tracking: Event-based information organization, pages 1–16, Norwell, MA, USA, 2002. Kluwer Academic Publishers. ISBN 0-7923-7664-1.
58> Gilad Mishne and Maarten de Rijke. Moodviews: Tools for blog mood analysis. In AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), pages 153–154, 2006.
59> Baruch Awerbuch and Robert Kleinberg. Competitive collaborative learning. In Proceedings of the Conference on Learning Theory (COLT), pages 233–248, 2005. Journal version to appear in Journal of Computer and System Sciences, special issue on computational learning theory.
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