Abstract
The problem of classifying sentences into various categories, arises frequently in text mining applications. One of the most important categorization of sentences observed in product reviews, movie reviews, blogs, customer feedbacks is - Suggestions, Appreciations and Complaints. We observed that the document classification techniques do not perform well for these three non-topical sentence classes. We propose to solve this problem using a supervised approach based on Dependency-based Word Subsequence Kernel and its variations. We compare the performance of our approach with the state-of-the-art short text classification techniques on 2 different datasets - Performance Appraisal comments and Product Reviews.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We use the Stanford Dependency Parser for the typed dependencies [17].
- 2.
To obtain the datasets, please contact the authors.
References
Li, Y., McLean, D., Bandar, Z.A., O’shea, J.D., Crockett, K.: Sentence similarity based on semantic nets and corpus statistics. IEEE Trans. Knowl. Data Eng. 18, 1138–1150 (2006)
Khoo, A., Marom, Y., Albrecht, D.: Experiments with sentence classification. In: Proceedings of the 2006 Australasian Language Technology Workshop, pp. 18–25 (2006)
Kate, R.J.: A dependency-based word subsequence kernel. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 400–409. Association for Computational Linguistics (2008)
Deshpande, S., Palshikar, G.K., Athiappan, G.: An unsupervised approach to sentence classification. In: COMAD, p. 88 (2010)
Goldberg, A.B., Fillmore, N., Andrzejewski, D., Xu, Z., Gibson, B., Zhu, X.: May all your wishes come true: a study of wishes and how to recognize them. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 263–271. Association for Computational Linguistics (2009)
Pan, F.: Multi-dimensional Fragment Classification in Biomedical Text. Queen’s University, Kingston (2006)
Mukherjee, A., Liu, B.: Modeling review comments. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1, pp. 320–329. Association for Computational Linguistics (2012)
Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., Potts, C.: A computational approach to politeness with application to social factors. arXiv preprint arXiv:1306.6078 (2013)
Wiebe, J., Riloff, E.: Creating subjective and objective sentence classifiers from unannotated texts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 486–497. Springer, Heidelberg (2005)
Wagner, J., Foster, J., van Genabith, J.: Judging grammaticality: experiments in sentence classification. CALICO J. 26, 474–490 (2013)
Kadoya, Y., Morita, K., Fuketa, M., Oono, M., Atlam, E.S., Sumitomo, T., Aoe, J.I.: A sentence classification technique using intention association expressions. Int. J. Comput. Math. 82, 777–792 (2005)
Yamamoto, Y., Takagi, T.: A sentence classification system for multi biomedical literature summarization. In: 21st International Conference on Data Engineering Workshops, pp. 1163–1163. IEEE (2005)
He, Q., Chang, K., Lim, E.P.: Anticipatory event detection via sentence classification. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2006, vol. 2, pp. 1143–1148. IEEE (2006)
Hachey, B., Grover, C.: Sequence modelling for sentence classification in a legal summarisation system. In: Proceedings of the 2005 ACM symposium on Applied Computing, pp. 292–296. ACM (2005)
Kim, Y.: Convolutional neural networks for sentence classification, pp. 1746–1751 (2014)
Baroni, M., Bernardini, S., Ferraresi, A., Zanchetta, E.: The wacky wide web: a collection of very large linguistically processed web-crawled corpora. Lang. Resour. Eval. 43, 209–226 (2009)
De Marneffe, M.C., MacCartney, B., Manning, C.D., et al.: Generating typed dependency parses from phrase structure parses. In: Proceedings of LREC, vol. 6, pp. 449–454 (2006)
McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM Conference on Recommender systems, pp. 165–172. ACM (2013)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 27 (2011)
Yu, H., Ho, C., Juan, Y., Lin, C.: Libshorttext: a library for short-text classification and analysis. Technical report (2013). http://www.csie.ntu.edu.tw/~cjlin/papers/libshorttext.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Pawar, S., Ramrakhiyani, N., Palshikar, G.K., Hingmire, S. (2015). Deciphering Review Comments: Identifying Suggestions, Appreciations and Complaints. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-19581-0_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19580-3
Online ISBN: 978-3-319-19581-0
eBook Packages: Computer ScienceComputer Science (R0)