Abstract
In this paper, we extend our previous work on social recommender systems to harness knowledge from product reviews. By mining product reviews, we can exploit sentiment-rich content to ascertain user opinion expressed over product aspects. Aspect aware sentiment analysis provides a more structured approach to product comparison. However, aspects extracted using NLP-based techniques remain too large and lead to poor quality product comparison metrics. To overcome this problem, we explore the utility of feature selection heuristics based on frequency counts and Information Gain (IG) to rank and select the most useful aspects. Here an interesting contribution is the use of top ranked products from Amazon to formulate a binary classification over products to form the basis for the supervised IG metric. Experimental results on three related product families (Compact Cameras, DSLR Cameras and Point & Shoot Cameras) extracted from Amazon.com demonstrate the effectiveness of incorporating feature selection techniques for aspect selection in recommendation task.
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Notes
- 1.
Sentences are parsed using the Stanford Dependency parser [3].
References
Chen, L., Chen, G., Wang, F.: Recommender systems based on user reviews: the state of the art. User Model. User-Adap. Interact. 25(2), 99–154 (2015)
Chen, Y.Y., Ferrer, X., Wiratunga, N., Plaza, E.: Sentiment and preference guided social recommendation. In: Lamontagne, L., Plaza, E. (eds.) ICCBR 2014. LNCS, vol. 8765, pp. 79–94. Springer, Heidelberg (2014)
De Marneffe, M., MacCartney, B., Manning, C., et al.: Generating typed dependency parses from phrase structure parses. In: Proceedings of Language Resources and Evaluation Conference, pp. 449–454 (2006)
Dong, R., Schaal, M., OMahony, M., McCarthy, K., Smyth, B.: Opinionated product recommendation. In: International Conference on Case-Based Reasoning (2013)
Hu, M., Liu, B.: Mining and summarising customer reviews. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 168–177 (2004)
Huang, J., Etzioni, O., Zettlemoyer, L., Clark, K., Lee, C.: Revminer: an extractive interface for navigating reviews on a smartphone. In: Proceedings of the 25th Annual ACM Symposium on User Interface Software And Technology, pp. 3–12. ACM (2012)
Lops, P., De Gemmis, M., Semeraro, G.: Content-based recommender systems: State of the art and trends. In: Ricci, R., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer US, New York (2011)
Moghaddam, S., Ester, M.: On the design of lda models for aspect-based opinion mining. In: Proceedings International Conference on Information and Knowledge Management, CIKM 2012 (2012)
Muhammad, A., Wiratunga, N., Lothian, R., Glassey, R.: Contextual sentiment analysis in social media using high-coverage lexicon. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems, pp. 79–93. Springer, Switzerland (2013)
Owoputi, O., O’Connor, B., Dyer, C., Gimpel, K., Schneider, N., Smith, N.A.: Improved part-of-speech tagging for online conversational text with word clusters. In: Association for Computational Linguistics (2013)
Ronen, R., Koenigstein, N., Ziklik, E., Nice, N.: Selecting content-based features for collaborative filtering recommenders. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 407–410. ACM (2013)
Smyth, B.: Case-based recommendation. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 342–376. Springer, Heidelberg (2007)
Vargas-Govea, B., González-Serna, G., Ponce-Medellın, R.: Effects of relevant contextual features in the performance of a restaurant recommender system. ACM RecSys 11 (2011)
Wiratunga, N., Koychev, I., Massie, S.: Feature selection and generalisation for retrieval of textual cases. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 806–820. Springer, Heidelberg (2004)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: ICML, vol. 97, pp. 412–420 (1997)
Zha, Z.-J., Jianxing, Y., Tang, J., Wang, M., Chua, T.-S.: Product aspect ranking and its applications. IEEE Trans. Knowl. Data Eng. 26(5), 1211–1224 (2014)
Acknowledgments
This research has been partially supported by AGAUR Scholarship (2013FI-B 00034) and NASAID (CSIC Intramural 201550E022).
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Chen, Y.Y., Ferrer, X., Wiratunga, N., Plaza, E. (2015). Aspect Selection for Social Recommender Systems. In: Hüllermeier, E., Minor, M. (eds) Case-Based Reasoning Research and Development. ICCBR 2015. Lecture Notes in Computer Science(), vol 9343. Springer, Cham. https://doi.org/10.1007/978-3-319-24586-7_5
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DOI: https://doi.org/10.1007/978-3-319-24586-7_5
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