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Performance and trends in recent opinion retrieval techniques

Published online by Cambridge University Press:  03 May 2013

Sylvester O. Orimaye ,
Saadat M. Alhashmi  and
Eu-Gene Siew
Sylvester O. Orimaye
Affiliation:
Faculty of Information Technology, Monash University, Jalan Lagoon Selatan, 46150 Bandar Sunway, Selangor Darul Ehsan, Malaysia; e-mail: [email protected], [email protected], [email protected]
Saadat M. Alhashmi
Affiliation:
Faculty of Information Technology, Monash University, Jalan Lagoon Selatan, 46150 Bandar Sunway, Selangor Darul Ehsan, Malaysia; e-mail: [email protected], [email protected], [email protected]
Eu-Gene Siew
Affiliation:
Faculty of Information Technology, Monash University, Jalan Lagoon Selatan, 46150 Bandar Sunway, Selangor Darul Ehsan, Malaysia; e-mail: [email protected], [email protected], [email protected]
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Abstract

This paper presents trends and performance of opinion retrieval techniques proposed within the last 8 years. We identify major techniques in opinion retrieval and group them into four popular categories. We describe the state-of-the-art techniques for each category and emphasize on their performance and limitations. We then summarize with a performance comparison table for the techniques on different datasets. Finally, we highlight possible future research directions that can help solve existing challenges in opinion retrieval.

Type
Articles
Information
The Knowledge Engineering Review , Volume 30 , Issue 1 , January 2015 , pp. 76 - 105
Copyright
Copyright © Cambridge University Press 2013 

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References

Abbasi, A., Chen, H., Salem, A. 2008. Sentiment analysis in multiple languages: feature selection for opinion classification in Web forums. ACM Transactions on Information Systems 26(3), 1–34.Google Scholar
Agarwal, N., Liu, H. 2008. Blogosphere: research issues, tools, and applications. SIGKDD Explorations Newsletter 10(1), 18–31.CrossRefGoogle Scholar
Amati, G., Rijsbergen, C. J. V. 2002. Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Transactions on Information Systems 20(4), 357–389.CrossRefGoogle Scholar
Baharudin, B., Lee, L. H., Khan, K. 2010. A review of machine learning algorithms for text-documents classification. Journal of Advances in Information Technology 1(1), 4–20.CrossRefGoogle Scholar
Bermingham, A., Smeaton, A. F. 2009. A study of inter-annotator agreement for opinion retrieval. In Proceedings of the 32nd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 784–785.Google Scholar
Blei, D. M., Ng, A. Y., Jordan, M. I. 2003. Latent dirichlet allocation. Journal of Machine Learning and Research 3, 993–1022.Google Scholar
Bollen, J., Pepe, A., Mao, H. 2010. Modeling public mood and emotion: Twitter sentiment and socioeconomic phenomena. In Proceedings of the 19th International Conference on World Wide Web, Raleigh, North Carolina.Google Scholar
Choi, Y., Kim, Y., Myaeng, S-H. 2009. Domain-specific sentiment analysis using contextual feature generation. In Proceeding of the 1st international CIKM Workshop on Topic-sentiment Analysis for Mass Opinion. ACM, 37–44.Google Scholar
Chung, C. K., Jones, C., Liu, A., Pennebaker, J. W. 2008. Predicting success and failure in weight loss blogs through natural language use. Association for the Advancement of Artificial Intelligence, 180–181.Google Scholar
Collins, M. J. 1996. A new statistical parser based on bigram lexical dependencies. In Proceedings of the 34th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 184–191.Google Scholar
Ding, X., Liu, B. 2007. The utility of linguistic rules in opinion mining. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 811–812.Google Scholar
Ding, X., Liu, B., Yu, P. S. 2008. A holistic lexicon-based approach to opinion mining. In Proceedings of the International Conference on Web Search and Web Data Mining. ACM, 231–240.Google Scholar
Du, W., Tan, S. 2009a. Building domain-oriented sentiment lexicon by improved information bottleneck. In Proceedings of the 18th ACM Conference on Information and Knowledge Management. ACM, 1749–1752.Google Scholar
Du, W., Tan, S. 2009b. An iterative reinforcement approach for fine-grained opinion mining. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Boulder, Colorado, 486–493.Google Scholar
Eguchi, K., Lavrenko, V. 2006. Sentiment retrieval using generative models. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 345–354.Google Scholar
Elsas, J., Aguello, J., Callan, J., Carbonell, J. 2007. Retrieval and Feedback Models for Blog Distillation. In Proceedings of the Text Retrieval Conference (TREC), National Institute of Standards and Technology, MD, USA.CrossRefGoogle Scholar
Elsas, J. L., Dumais, S. T. 2010. Leveraging temporal dynamics of document content in relevance ranking. In Proceedings of the Third ACM International Conference on Web search and Data Mining. ACM, 1–10.Google Scholar
Esuli, A. 2008. Automatic generation of lexical resources for opinion mining: models, algorithms and applications. SIGIR Forum 42(2), 105–106.CrossRefGoogle Scholar
Esuli, A., Sebastiani, F. 2006. SENTIWORDNET: a publicly available lexical resource for opinion mining. In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC06), Italy.Google Scholar
Fei, Z., Liu, J., Wu, G. 2004. Sentiment classification using phrase patterns. In Proceedings of the 4th IEEE International Conference on Computer Information Technology, 1147–1152.Google Scholar
Fellbaum, C. 2010. WordNet. Theory and Applications of Ontology. In Computer Applications, Poli, R., Healy, M. & Kameas, A. (eds). Springer, 231–243.Google Scholar
Fortuna, B., Rodrigues, E. M., Milic-Frayling, N. 2007. Improving the classification of newsgroup messages through social network analysis. In Proceedings of the 16th ACM Conference on Information and Knowledge Management. ACM, 877–880.Google Scholar
Gamon, M. 2004. Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In Proceedings of the 20th international conference on Computational Linguistics. ACL, 841.Google Scholar
Ganapathibhotla, M., Liu, B. 2008. Mining opinions in comparative sentences. In Proceedings of the 22nd International Conference on Computational Linguistics, Association for Computational Linguistics, vol. 1, 241–248.Google Scholar
Gerani, S., Carman, M. J., Crestani, F. 2009. Investigating learning approaches for Blog Post opinion retrieval. In Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval. Springer-Verlag, 313–324.Google Scholar
Gerani, S., Carman, M. J., Crestani, F. 2010. Proximity-based opinion retrieval. In Proceedings of the 33rd International Special Interest Group on Information Retrieval (SIGIR) Conference on Research and Development in Information Retrieval. ACM, 403–410.Google Scholar
Hannah, D., Macdonald, C., Peng, J., He, B., Ounis, I. 2007. University of Glasgow at Trec 2007: Experiments in Blog and Enterprise Tracks with Terrier. TREC.Google Scholar
Hatzivassiloglou, V., McKeown, K. R. 1997. Predicting the semantic orientation of adjectives. In Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 174–181.Google Scholar
He, B., Macdonald, C., He, J., Ounis, I. 2008. An effective statistical approach to blog post opinion retrieval. In Proceeding of the 17th ACM Conference on Information and Knowledge Management. ACM, 1063–1072.Google Scholar
Hiemstra, D. 2000. Using language models for information retrieval. PhD Thesis, Centre for Telematics and Information Technology.Google Scholar
Hu, M., Liu, B. 2004. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 168–177.Google Scholar
Hu, M., Liu, B. 2004. Mining opinion features in customer reviews. In Proceedings of the 19th National Conference on Artifical Intelligence. AAAI Press, 755–760.Google Scholar
Huang, J., Efthimiadis, E. N. 2009. Analyzing and evaluating query reformulation strategies in web search logs. In Proceeding of the 18th ACM Conference on Information and Knowledge Management. ACM, 77–86.Google Scholar
Huang, X., Croft, W. B. 2009. A unified relevance model for opinion retrieval. In Proceeding of the 18th ACM Conference on Information and Knowledge Management. ACM, 947–956.Google Scholar
Jia, L., Yu, C., Meng, W. 2009. The effect of negation on sentiment analysis and retrieval effectiveness. In Proceeding of the 18th ACM Conference on Information and Knowledge Management. ACM, 1827–1830.Google Scholar
Jiang, P., Zhang, C., Yang, Q., Niu, Z. 2010. Blog opinion retrieval based on topic-opinion mixture model. In Advances in Knowledge Discovery and Data Mining, Zaki, M., Yu, J., Ravindran, B. & Pudi, V. (eds). Springer, vol. 6119, 249–260.CrossRefGoogle Scholar
Jin, W., Ho, H. H., Srihari, R. K. 2009. OpinionMiner: a novel machine learning system for web opinion mining and extraction. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1195–1204.Google Scholar
Jindal, N., Liu, B. 2006. Identifying comparative sentences in text documents. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 244–251.Google Scholar
Jia, L., Yu, C., Zhang, W. 2008. UIC at TREC 2008 Blog Track. In Text REtrieval Conference 2008.Google Scholar
Jones, R., Rey, B., Madani, O., Greiner, W. 2006. Generating query substitutions. In Proceedings of the 15th International Conference on World Wide Web, Edinburgh, Scotland, 387–396.Google Scholar
Joshi, H., Bayrak, C., Xu, X. 2006. UALR at TREC: Blog track. In The Fifteenth Text REtrieval Conference Proceedings (TREC 2006), National Institute of Standards and Technology (NIST), Gaithersburg, MD.Google Scholar
Kanayama, H., Nasukawa, T. 2006. Fully automatic lexicon expansion for domain-oriented sentiment analysis. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 355–363.Google Scholar
Kim, J., Li, J.-J., Lee, J.-H. 2009. Discovering the discriminative views: measuring term weights for sentiment analysis. In 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, Association for Computational Linguistics, vol. 1, 253–261.Google Scholar
Kobayakawa, T. S., Kumano, T., Tanaka, H., Okasaki, N., Kim, J.-D., Tsujii, J. 2009. Opinion classification with tree kernel SVM using linguistic modality analysis. In Proceeding of the 18th ACM conference on Information and knowledge management, Hong Kong, China, 1791–1794.Google Scholar
Koppel, M., Shtrimberg, I. 2006. Good news or bad news? Let the market decide. Computing attitude and affect in text. In Theory and Applications, Shanahan, J., Qu, Y. & Wiebe, J. (eds). Springer, vol. 20, 297–301.Google Scholar
Krahmer, E. 2010. What computational linguists can learn from psychologists (and vice versa). Association for Computational Linguistics 36(2), 285–294.CrossRefGoogle Scholar
Ku, L.-W., Liang, Y.-T., Chen, H.-H. 2006. Opinion Extraction, Summarization and Tracking in News and Blog Corpora. American Association for Artificial Intelligence.Google Scholar
Lavrenko, V. P. 2010. Introduction to probabilistic models in IR. In Proceeding of the 33rd international ACM SIGIR Conference on Research and Development in Information Retrieval, Geneva, Switzerland, 905–905.Google Scholar
Lee, S.-W., Lee, J.-T., Song, Y.-I., Rim, H.-C. 2010. High precision opinion retrieval using sentiment-relevance flows. In Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Geneva, Switzerland, 817–818.Google Scholar
Lee, Y., Jung, H.-y., Song, W., Lee, G.-H. 2010. Mining the blogosphere for top news stories identification. In Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Geneva, Switzerland, 395–402.Google Scholar
Lee, Y., Na, S.-H., Kim, J., Nam, S.-H., Jung, H.-y., Lee, J.-H. 2008. KLE at TREC 2008 Blog Track: Blog Post and Feed Retrieval. In TREC 2008.Google Scholar
Lin, C., He, Y. 2009. Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM CIKM. ACM, 375–384.Google Scholar
Lin, D. 1998. Dependency-Based Evaluation of Minipar. http://www.cfilt.iitb.ac.in/archives/minipar_evaluation.pdfGoogle Scholar
Lin, W.-H., Wilson, T., Wiebe, J., Hauptmann, A. 2006. Which side are you on?: identifying perspectives at the document and sentence levels. In Proceedings of the Tenth Conference on Computational Natural Language Learning. Association for Computational Linguistics, 109–116.Google Scholar
Lim, E.-P., Nguyen, V.-A., Jindal, N., Liu, B., Lauw, H. W. 2010. Detecting product review spammers using rating behaviors. In Proceedings of CIKM ACM, Toronto, Ontario, Canada.CrossRefGoogle Scholar
Liu, B. 2010. Sentiment analysis and subjectivity. Handbook of Natural Language Processing 2, 568.Google Scholar
Liu, B. 2010. Sentiment analysis: a multi-faceted problem. IEEE Intelligent Systems 25(3), 76–80.Google Scholar
Liu, B., Hu, M., Cheng, J. 2005. Opinion observer: analyzing and comparing opinions on the Web. In Proceedings of the 14th International Conference on World Wide Web, Chiba, Japan, 342–351.Google Scholar
Liu, S., Liu, F., Yu, C., Meng, Y. 2004. An effective approach to document retrieval via utilizing WordNet and recognizing phrases. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Sheffield, UK, 266–272.Google Scholar
Lloret, E., Saggion, H., Palomar, M. 2010. Experiments on summary-based opinion classification. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text. ACL, 107–115.Google Scholar
Lu, B., Ott, M., Cardie, C., Tsou, B. K. 2011. Multi-aspect sentiment analysis with topic models. 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW), Vancouver, Canada, 81–88.Google Scholar
Macdonald, C., Ounis, I., Soboroff, I. 2007. Overview of the TREC2007 Blog Track. TREC 2007.Google Scholar
Macdonald, C., Ounis, I., Soboroff, I. 2009. Overview of the TREC2009 Blog Track. TREC 2009.Google Scholar
Macdonald, C., Santos, R. L. T., Ounis, I., Soboroff, I. 2010. Blog track research at TREC. SIGIR Forum 44(1), 58–75.CrossRefGoogle Scholar
McCreadie, R., Macdonald, C., Ounis, I., Peng, J., Santos, R. L. 2009. University of Glasgow at Trec 2009: Experiments with Terrier. Glasgow University, UK.Google Scholar
Manning, C. D., Raghavan, P., Schtze, H. 2009. An Introduction to Information Retrieval. Cambridge University Press.Google Scholar
Mishne, G. 2006. Multiple ranking strategies for opinion retrieval in blogs. In Online Proceedings of TREC.Google Scholar
Munson, S. A., Resnick, P. 2010. Presenting diverse political opinions: how and how much. In Proceedings of the 28th international Conference on Human Factors in Computing Systems. ACM, 1457–1466.Google Scholar
Na, S.-H., Lee, Y., Nam, S.-H., Lee, J.-H. 2009. Improving opinion retrieval based on query-specific sentiment lexicon. In Advances in Information Retrieval, Boughanem, M., Berrut, C., Mothe, J. & Soule-Dupuy, C. (eds). Springer, vol. 5478, 734–738.CrossRefGoogle Scholar
Narayanan, R., Liu, B., Choudhary, A. 2009. Sentiment analysis of conditional sentences. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 180–189.Google Scholar
Nasukawa, T., Yi, J. 2003. Sentiment analysis: capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture. ACM, 70–77.Google Scholar
Nguyen, T., Phung, D., Adams, B., Tran, T., Venkatesh, S. 2010. Classification and pattern discovery of mood in Weblogs. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 283–290.Google Scholar
O'Hare, N., Davy, M., Bermingham, A., Ferguson, P., Sheridan, P., Gurrin, C., Smeaton, A. F. 2009. Topic-dependent sentiment analysis of financial blogs. In Proceeding of the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion. ACM, 9–16.Google Scholar
Ounis, I., Macdonald, C., Soboroff, I. 2008. Overview of the TREC2008 Blog Track. TREC 2008.Google Scholar
Ounis, I., de Rijke, M., Macdonald, C., Mishne, G., Soboroff, I. 2006. Overview of the TREC-2006 Blog Track. TREC 2006.Google Scholar
Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., Chen, Z. 2010. Cross-domain sentiment classification via spectral feature alignment. In Proceedings of the 19th International Conference on World Wide Web. ACM, 751–760.Google Scholar
Pang, B., Lee, L. 2004. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 271.Google Scholar
Pang, B., Lee, L. 2008. Opinion mining and sentiment analysis. Foundation and Trends in Information Retrieval 2(1–2), 1–135.CrossRefGoogle Scholar
Pang, B., Lee, L., Vaithyanathan, S. 2002. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 79–86.Google Scholar
Paul, M., Girju, R. 2010. A two-dimensional topic-aspect model for discovering multi-faceted topics. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, Georgia, USA, 545–550.Google Scholar
Pickens, J., MacFarlane, A. 2006. Term context models for information retrieval. In Proceedings of the 15th ACM international Conference on Information and KnowledgeMmanagement. ACM, 559–566.Google Scholar
Robertson, S. E. 1997. Readings in Information Retrieval. Morgan Kaufmann Publishers Inc., 281–286.Google Scholar
Robertson, S., Zaragoza, H. 2009. The probabilistic relevance framework: BM25 and beyond. Foundations and Trends in Information Retrieval 3(4), 333–389.CrossRefGoogle Scholar
Robertson, S. E., Walker, S. 1999. Okapi/keenbow at trec-8. In Proceedings of TREC, volume 8.Google Scholar
Sarmento, L., Carvalho, P., Silva, M. J., de Oliveira, E. 2009. Automatic creation of a reference corpus for political opinion mining in user-generated content. In Proceedings of the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion, Hong Kong, 29–36.Google Scholar
Santos, R. L. T., Ben, H., Macdonald, C., Ounis, I. 2009. Integrating proximity to subjective sentences for blog opinion retrieval. In Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval. Springer-Verlag, 325–336.Google Scholar
Santos, R. L. T., Macdonald, C., Ounis, I. 2010. Exploiting query reformulations for web search result diversification. In Proceedings of the 19th International Conference on World Wide Web. ACM, 881–890.Google Scholar
Siersdorfer, S., Chelaru, S., Pedro, J.-S. 2010. How useful are your comments? Analyzing and predicting YouTube comments and comment ratings. International World Wide Web Conference, Raleigh, North Carolina, USA, 891–900.Google Scholar
Sparck Jones, K., Walker, S., Robertson, S. E. 2000a. A probabilistic model of information retrieval: development and comparative experiments: Part 1. Information Processing & Management 36(6), 779–808.CrossRefGoogle Scholar
Sparck Jones, K., Walker, S., Robertson, S. E. 2000b. A probabilistic model of information retrieval: development and comparative experiments: Part 2. Information Processing & Management 36(6), 809–840.CrossRefGoogle Scholar
Stone, P. J., Dunphy, D. C., Smith, M. S. 1966. The general inquirer: a computer approach to content analysis. MIT Press.Google Scholar
Strapparava, C., Mihalcea, R. 2008. Learning to identify emotions in text. In Proceedings of the 2008 ACM Symposium on Applied Computing. ACM, 1556–1560.Google Scholar
Sun, A., Lim, E.-P., Ng, W.-K. 2002. Web classification using support vector machine. In Proceedings of the 4th International Workshop on Web Information and Data Management. ACM, 96–99.Google Scholar
Taboada, M., Brooke, J., Stede, M. 2009. Genre-based paragraph classification for sentiment analysis. In Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics, 62–70.Google Scholar
Tan, B., Peng, F. 2008. Unsupervised query segmentation using generative language models and wikipedia. In Proceedings of the 17th International Conference on World Wide Web. ACM, 347–356.Google Scholar
Tan, S., Wang, Y., Cheng, X. 2008. Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 743–744.Google Scholar
Tausczik, Y. R., Pennebaker, J. W. 2010. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology 29(1), 24–54.CrossRefGoogle Scholar
Thet, T. T., Na, J.-C., Khoo, C. S. G., Shakthikumar, S. 2009. Sentiment analysis of movie reviews on discussion boards using a linguistic approach. In Proceeding of the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion. ACM, 81–84.Google Scholar
Torsten, Z., Christof, M., Iryna, G. 2009. Extracting Lexical Semantic Knowledge from Wikipedia and Wiktionary, LREC.Google Scholar
Tumasjan, A., Sprenger, T. O., Sandner, P. G., Welpe, I. M. 2010. Predicting elections with twitter: what 140 characters reveal about political sentiment. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, Washington, DC.CrossRefGoogle Scholar
Turney, P., Littman, M. L. 2002. Unsupervised learning of semantic orientation from a hundred-billion-word corpus. Technical Report, EGB-1094, National Research Council Canada.Google Scholar
Vechtomova, O. 2010. Facet-based opinion retrieval from blogs. Information Processing & Management 46(1), 71–88.CrossRefGoogle Scholar
Whitelaw, C., Garg, N., Argamon, S. 2005. Using appraisal groups for sentiment analysis. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management, Bremen, Germany. ACM, 625–631.Google Scholar
Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., Patwardhan, S. 2005. OpinionFinder: a system for subjectivity analysis. In Proceedings of HLT/EMNLP on Interactive Demonstrations. Association for Computational Linguistics, 34–35.Google Scholar
Xu, X., Liu, Y., Xu, H., Yu, X., Song, L., Guan, F., Peng, Z., Cheng, X. 2009. ICTNET at Blog Track TREC 2009. In TREC 2009.Google Scholar
Yang, C., Lin, K. H.-Y., Chen, H.-H. 2007. Building emotion lexicon from weblog corpora. In Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions. Association for Computational Linguistics, 133–136.Google Scholar
Yu, S., Cai, D., Wen, J.-R., Ma, W.-Y. 2003. Improving pseudo-relevance feedback in web information retrieval using web page segmentation. In Proceedings of the 12th International Conference on World Wide Web. ACM, 11–18.Google Scholar
Yu, X., Liu, Y., Huang, X., An, A. 2010. A quality-aware model for sales prediction using reviews. In Proceedings of the 19th International Conference on World wide web. ACM, 1217–1218.Google Scholar
Zafarani, R., Cole, W., Huan, L. 2010. Sentiment propagation in social networks: a case study in LiveJournal. In Advances in Social Computing, Chai, S.-K., Salerno, J. & Mabry, P. (eds). Springer, vol. 6007, 413–420.CrossRefGoogle Scholar
Zagibalov, T., Carroll, J. 2008. Automatic seed word selection for unsupervised sentiment classification of Chinese text. In Proceedings of the 22nd International Conference on Computational Linguistics. Association for Computational Linguistics, 1073–1080.Google Scholar
Zettlemoyer, L. S., Collins, M. 2009. Learning context-dependent mappings from sentences to logical form. In 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. Association for Computational Linguistics, vol. 2, 976–984.Google Scholar
Zhang, C., Xue, G.-R., Yu, Y., Zha, H. 2009. Web-scale classification with naive bayes. In Proceedings of the 18th international Conference on World Wide Web. ACM, 1083–1084.Google Scholar
Zhang, E., Zhang, Y. 2006. UCSC on TREC 2006 Blog Opinion Mining. In TREC 2006.Google Scholar
Zhang, M., Ye, X. 2008. A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 411–418.Google Scholar
Zhang, W., Liu, S., Yu, C., Sun, C., Liu, F., Meng, W. 2007a. Recognition and classification of noun phrases in queries for effective retrieval. In Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management. ACM, 711–720.Google Scholar
Zhang, W., Yu, C. 2006. UIC at TREC 2006 Blog Track. In TREC, 2006.Google Scholar
Zhang, W., Yu, C., Meng, W. 2007b. Opinion retrieval from blogs. In Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management. ACM, 831–840.Google Scholar