Opinion-based entity ranking

Information Retrieval

Volumn 15, Issue 2, 2012, Pages 116-150

  Ganesan, Kavita ^a   Zhai, ChengXiang ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

Ad hoc faceted navigation; Entity oriented search; Opinion matching; Preference based entity search; Product search; Vertical search

Indexed keywords

EID: 84858797512     PISSN: 13864564     EISSN: 15737659     Source Type: Journal    
DOI: 10.1007/s10791-011-9174-8     Document Type: Article

References (35)

1. Amati, G., & van Rijsbergen, C. J. (2002). Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Transactions on Information System, 20(4), 357-389.
2. Balog, K., Azzopardi, L., & de Rijke, M. (2009). A language modeling framework for expert finding. Information Processing & Management, 45(1), 1-19.
3. Dave, K., Lawrence, S., & Pennock, D. M. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In WWW '03: proceedings of the twelfth international conference on World Wide Web (pp. 519-528). ACM Press.
4. Fang, H., Tao, T., & Zhai. C. (2004). A formal study of information retrieval heuristics. In SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval (ppp. 49-56). New York, NY, USA: ACM Press.
5. Fang, H., & Zhai, C. (2007). Probabilistic models for expert finding. In ECIR (pp. 418-430).
6. 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 (p. 841). Geneva, Switzerland: Association for Computational Linguistics.
7. Hannah, J. P. B. H. I. O. D., & Macdonald, C. (2007). University of Glasgow at TREC2007: Experiments in blog and enterprise tracks with Terrier. In Proceeddings of the 16th text retrieval conference(TREC 2007).
8. He, B., Macdonald, C., He, J., & Ounis, I. (2008). An effective statistical approach to blog post opinion retrieval. In CIKM '08: proceeding of the 17th ACM conference on information and knowledge management (pp. 1063-1072). New York, NY, USA: ACM.
9. Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information System, 20(4), 422-446.
10. Koren, J., Zhang, Y., Liu, X. (2008). Personalized interactive faceted search. In WWW '08: proceeding of the 17th international conference on World Wide Web (pp. 477-486). New York, NY, USA: ACM.
11. Krulwich, B., & Burkey, C. (1996). The contactfinder agent: Answering bulletin board questions with referrals. In AAAI/IAAI (Vol. 1, pp. 10-15).
12. Lafferty, J., & Zhai, C. (2001). Document language models, query models, and risk minimization for information retrieval. In SIGIR '01: proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 111-119). New York, NY, USA: ACM.
13. Lu, Y., Zhai, C., & Sundaresan, N. (2009). Rated aspect summarization of short comments. In Proceedings of the 18th international conference on World wide web (pp. 131-140). Madrid, Spain: ACM.
14. Nasukawa, T., & Yi, J. (2003). Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture (pp. 70-77). Sanibel Island, FL, USA: ACM.
15. Ounis, I., Amati, G., Plachouras, V., He, B., Macdonald, C., & Lioma, C. (2006). Terrier: A high performance and scalable information retrieval platform. In Proceedings of ACM SIGIR'06 workshop on open source information retrieval (OSIR 2006).
16. Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the 2002 conference on empirical methods in natural language processing (EMNLP) (pp. 79-86).
17. Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the ACL (pp. 271-278).
18. Pang, B., & Lee, L. (2005). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the ACL (pp. 115-124).
19. Ponte, J. M., & Croft, W. B. (1998). A language modeling approach to information retrieval. In SIGIR '98: proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval (pp. 275-281). New York, NY, USA: ACM.
20. Prabowo, R., & Thelwall, M. (2009). Sentiment analysis: A combined approach. Journal of Informetrics, 3(2), 143-157.
21. Robertson, S. (2009). The probabilistic relevance framework: BM25 and beyond. Foundations and Trends® in Information Retrieval, 3(4), 333-389.
22. Robertson, S. E., Walker, S., Jones, S., Hancock-Beaulieu, M., & Hancock-Beaulieu, M. (1994). Okapi at TREC-3. In TREC (p. 109).
23. Sadikov, E., Madhavan, J., Wang, L., & Halevy, A. (2010). Clustering query refinements by user intent. In WWW '10: Proceedings of the 19th international conference on World wide web (pp. 841-850). New York, NY, USA: ACM.
24. Salton, G., & Buckley, C. (1997). Improving retrieval performance by relevance feedback, pp. 355-364.
25. Siegel, S., & Castellan, N. J. (1988). Nonparametric statistics for the social sciences. New York: McGraw-Hill.
26. Snyder, B., & Barzilay, R. (2007). Multiple aspect ranking using the good grief algorithm. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL) (pp. 300-307).
27. Tan, B., & Peng, F. (2008). Unsupervised query segmentation using generative language models and wikipedia. In WWW '08: proceeding of the 17th international conference on World Wide Web (pp. 347-356). New York, NY, USA: ACM.
28. Tunkelang, D. (2009). Faceted search. San Rafael: Morgan and Claypool Publishers.
29. Turney, P. D., & Littman, M. L. (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information System, 21(4), 315-346.
30. Wang, H., Lu, Y., & Zhai, C. (2010). Latent aspect rating analysis on review text data: A rating regression approach. In KDD '10: proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 783-792). New York, NY, USA: ACM.
31. Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83.
32. Yang, K., Yu, N., Valerio, A., Zhangm, H., & Ke, W. (2007). Fusion approach to finding opinions in blogosphere. ICWSM.
33. Zhai, C. (2008). Statistical language models for information retrieval. San Rafael: Morgan & Claypool.
34. Zhai, C., Lafferty, J. (2004). A study of smoothing methods for language models applied to information retrieval. ACM Transactions on Information System, 22(2), 179-214.
35. Zhai, C., Lafferty, J. (2001). A study of smoothing methods for language models applied to ad hoc information retrieval. In SIGIR '01: proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 334-342). New York, NY, USA: ACM.