Abstract
With the number of reviews growing every day, it has become more important for both consumers and producers to gather the information that these reviews contain in an effective way. For this, a well performing feature extraction method is needed. In this paper we focus on detecting explicit features. For this purpose, we use grammatical relations between words in combination with baseline statistics of words as found in the review text. Compared to three investigated existing methods for explicit feature detection, our method significantly improves the F 1-measure on three publicly available data sets.
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
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th International Conference on Very Large Databases (VLDB 1994), vol. 1215, pp. 487–499. Morgan Kaufmann (1994)
Androutsopoulos, I., Galanis, D., Manandhar, S., Papageorgiou, H., Pavlopoulos, J., Pontiki, M.: SemEval-2014 Task 4 (March 2014), http://alt.qcri.org/semeval2014/task4/
Bain, L.J., Engelhardt, M.: Introduction to Probability and Mathematical Statistics, 2nd edn. Duxbury Press (2000)
Bickart, B., Schindler, R.M.: Internet Forums as Influential Sources of Consumer Information. Journal of Interactive Marketing 15(3), 31–40 (2001)
Ganu, G., Elhadad, N., Marian, A.: Beyond the Stars: Improving Rating Predictions using Review Content. In: Proceedings of the 12th International Workshop on the Web and Databases (WebDB 2009) (2009)
Hai, Z., Chang, K., Kim, J.-J.: Implicit Feature Identification via Co-occurrence Association Rule Mining. In: Gelbukh, A.F. (ed.) CICLing 2011, Part I. LNCS, vol. 6608, pp. 393–404. Springer, Heidelberg (2011)
Hu, M., Liu, B.: Mining Opinion Features in Customer Reviews. In: Proceedings of the 19th National Conference on Artifical Intelligence (AAAI 2004), pp. 755–760. AAAI (2004)
Hu, M., Liu, B.: Mining and Summarizing Customer Reviews. In: Proceedings of 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), pp. 168–177. ACM (2004)
Leech, G., Rayson, P., Wilson, A.: Word Frequencies in Written and Spoken English: Based on the British National Corpus. Longman (2001)
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool (2012)
Marneffe, M.C.D., MacCartney, B., Manning, C.D.: Generating Typed Dependency Parses from Phrase Structure Parses. In: Proceedings of International Conference on Language Resources and Evaluation (LREC 2006), vol. 6, pp. 449–454 (2006)
Marneffe, M.C.D., Manning, C.D.: Stanford Typed Dependencies Manual (September 2008), http://nlp.stanford.edu/downloads/lex-parser.shtml
Miller, G., Beckwith, R., Felbaum, C., Gross, D., Miller, K.: Introduction to WordNet: An On-Line Lexical Database. International Journal of Lexicography 3(4), 235–312 (1990)
Nakagawa, H., Mori, T.: A Simple but Powerful Automatic Term Extraction Method. In: Proceedings of the 19th International Conference on Computational Linguistics (AAAI 2004), pp. 29–35. Morgan Kaufmann Press (2002)
Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)
Ross, S.M.: Introduction to Probability Models, 10th edn. Academic Press (2010)
Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H., Jin, C.: Red Opal: Product-Feature Scoring from Reviews. In: Proceedings of the 8th ACM Conference on Electronic Commerce (EC 2007), pp. 182–191. ACM (2007)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley (2005)
Wu, H., Salton, G.: A Comparison of Search Term Weighting: Term Relevance vs. Inverse Document Frequency. In: Proceedings of the 4th Annual International ACM Conference on Information Storage and Retrieval, pp. 30–39 (1981)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
de Boer, N., van Leeuwen, M., van Luijk, R., Schouten, K., Frasincar, F., Vandic, D. (2014). Identifying Explicit Features for Sentiment Analysis in Consumer Reviews. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8786. Springer, Cham. https://doi.org/10.1007/978-3-319-11749-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-11749-2_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11748-5
Online ISBN: 978-3-319-11749-2
eBook Packages: Computer ScienceComputer Science (R0)