Aspect-based Product Review Summarizer

Hsiang Hui Lek, Danny C. C. Poo

2012

Abstract

Consumers are now relying on product reviews websites to aid them in deciding which product to buy. These sites contain large number of reviews and reading through them is tedious. In this work, we propose building a product review summarizer which will process all the reviews for a product and present them in an easy to read manner. The generated summaries show a list of product features or aspects and their corresponding rating, allowing users in comparing between different products easily. Our system first makes use of an aspect/sentiment extractor to extract the list of aspects and their sentiment words. Sentiment classification is then performed to obtain the polarity of aspects. Finally, these aspects are combined and assigned a rating to form the final summary. The experimental results on various domains have shown that our system is promising.

References

  1. Du, W., Tan, S., Cheng, X., & Yun, X., 2010. Adapting information bottleneck method for automatic construction of domain-oriented sentiment lexicon. In Proceedings of the third ACM international conference on Web search and data mining. WSDM'10. pp. 111-120.
  2. Esuli, A. & Sebastiani, F., 2006. SentiWordNet: A publicly available lexical resource for opinion mining. In Proceedings of LREC-06.
  3. Fahrni, A. & Klenner, M., 2008. Old wine or warm beer: target-specific sentiment analysis of adjectives. In Symposium on Affective Language in Human and Machine, AISB 2008 Convention. pp. 60-63.
  4. Fellbaum, C. ed., 1998. WordNet: An Electronic Lexical Database, MIT Press.
  5. Girju, R., Badulescu, A. & Moldovan, D., 2006. Automatic Discovery of Part-Whole Relations. Comput. Linguist., 32(1), p.83-135.
  6. Hu, M. & Liu, B., 2004. Mining and summarizing customer reviews. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Seattle, Washington, pp. 168-177.
  7. Jo, Y. & Oh, A.H., 2011. Aspect and sentiment unification model for online review analysis. In Proceedings of the fourth ACM international conference on Web search and data mining. WSDM'11. pp. 815-824.
  8. Lau, R. Y. K., Zhang, W., Bruza, P. D., & Wong, K. F., 2011. Learning Domain-Specific Sentiment Lexicons for Predicting Product Sales. In 2011 IEEE 8th International Conference on e-Business Engineering (ICEBE). pp. 131 -138.
  9. Marneffe, M., Maccartney, B. & Manning, C., 2006. Generating Typed Dependency Parses from Phrase Structure Parses. In Proceedings of LREC-06. pp. 449-454.
  10. Moghaddam, S. & Ester, M., 2011. ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. SIGIR'11. pp. 665-674.
  11. Pang, B. & Lee, L., 2008. Opinion mining and sentiment analysis, Now Publishers.
  12. Popescu, A.-M. & Etzioni, O., 2005. Extracting Product Features and Opinions from Reviews. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP).
  13. Qiu, G., Liu, B., Bu, J., & Chen, C., 2009. Expanding Domain Sentiment Lexicon through Double Propagation. In International Joint Conference on Artificial Intelligence. pp. 1199-1204.
  14. Stone, P. J., 1966. The General Inquirer: A Computer Approach to Content Analysis, The MIT Press.
  15. Turney, P. D. & Littman, M. L., 2003. Measuring Praise and Criticism: Inference of Semantic Orientation from Association. ACM Transactions on Information Systems (TOIS), 21(4), pp.315-346.
  16. Wilson, T., Wiebe, J. & Hoffmann, P., 2005. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP). pp. 347-354.
  17. Zhang, L. & Liu, B., 2011. Identifying noun product features that imply opinions. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2. HLT'11. Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 575-580.
  18. Zhu, J., Zhang, C. & Ma, M., 2012. Multi-Aspect Rating Inference with Aspect-based Segmentation. Affective Computing, IEEE Transactions on, PP(99), p.1.
Download


Paper Citation


in Harvard Style

Hui Lek H. and C. C. Poo D. (2012). Aspect-based Product Review Summarizer . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2012) ISBN 978-989-8565-30-3, pages 290-295. DOI: 10.5220/0004170302900295


in Bibtex Style

@conference{keod12,
author={Hsiang Hui Lek and Danny C. C. Poo},
title={Aspect-based Product Review Summarizer},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2012)},
year={2012},
pages={290-295},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004170302900295},
isbn={978-989-8565-30-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2012)
TI - Aspect-based Product Review Summarizer
SN - 978-989-8565-30-3
AU - Hui Lek H.
AU - C. C. Poo D.
PY - 2012
SP - 290
EP - 295
DO - 10.5220/0004170302900295