Abstract
Nowadays, with the growth of information and data provided in Internet, it becomes too difficult for a user to read and understand all the reviews from huge amount of reviews. In today’s world, we purchase products, book movie tickets, book train tickets, book hotel rooms, and buy products from different websites. Users also share their views about product, hotel, news, and other topics on Web in the form of reviews, blogs, etc. We can found some basic reviews in user review and also can find user own opinions about the experience with various products. Many users read the reviews of the information given on the Web to take decisions such as buying products, watching movie, going to restaurant, etc. It is difficult for Web users to read and understand the contents from a large number of reviews. The important and useful information can be extracted from the reviews through opinion mining and summarization process. We obtained about 78.2% of accuracy of hotel review classification as positive or negative review by machine learning method. The classified and summarized hotel review information helps the Web users to understand the review contents easily in a short time.
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References
D. Jurafsky, J.H., Martin Speech Language Processing
Globally Normalized Transition-Based Neural Networks by
WaveNet: A Generative Model for Raw Audio by
K. Crammer, Y. Singer, Pranking with ranking, in Neural Information Processing Systems: Natural and Synthetic (NIPS), ed. by T.G. Dietterich, S. Becker, Z. Ghahramani (MIT Press, Vancouver, British Columbia, Canada, 2001), pp. 641–647
M. Mahajan, P. Nguyen, G. Zweig, Summarization of multiple user reviews in the restaurant domain. Technical Report (MSR-TR-2007-126), Sept 2007
A.B. Goldberg, J. Zhu, Seeing stars when there aren’t many stars: graph-based semisupervised learning for sentiment categorization (2006)
C.W. Leung, S.C. Chan, F. Chung, Integrating collaborative filtering and sentiment analysis: a rating inference approach, in Proceedings of The ECAI 2006 Workshop on Recommender Systems, Riva del Garda, vol. I (2006), pp. 62–66
B. Pang, L. Lee, Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales, in Proceedings of the Association for Computational Linguistics (ACL) (2005), pp. 115–124
B. Pang, L. Lee, S. Vaithyanathan, Thumbs up? Sentiment classification using machine learning techniques, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2002), pp. 79–86
K. Shimada, T. Endo, Seeing several stars: a rating inference task for a document containing several evaluation criteria, in Advances in Knowledge Discovery and Data Mining, 12th Pacific Asia Conference, PAKDD 2008, volume 5012 of Lecture Notes in Computer Science (Springer, Osaka, Japan, 2008), pp. 1006–1014
B. Snyder, R. 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) (2007), pp. 300–307
P. Turney, Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews, in Proceedings of the Association for Computational Linguistics (ACL) (2002), pp. 417–424
H. Yu, V. 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)
R. Malloy, Internet and Personal Computing Abstracts: IPCA, Volume 22 Issues, Information Today, Incorporated (2001)
J. Pan, S. Chen, N. Nguyen, Intelligent information and database systems, in 4th Asian Conference, ACIIDS, Proceedings Part 2, Kaohsiung, Taiwan (2012)
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Ghosh, D. (2020). A Sentiment-Based Hotel Review Summarization. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_5
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DOI: https://doi.org/10.1007/978-981-13-7403-6_5
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