Abstract
E-commerce reviews reveal the customers’ attitudes on the products, which are very helpful for customers to know other people’s opinions on interested products. Meanwhile, producers are able to learn the public sentiment on their products being sold in E-commerce platforms. Generally, E-commerce reviews involve many aspects of products, e.g., appearance, quality, price, logistics, and so on. Therefore, sentiment analysis on E-commerce reviews has to cope with those different aspects. In this paper, we define each of those aspects as a dimension of product, and present a multi-dimensional sentiment analysis approach for E-commerce reviews. In particular, we employ a sentiment lexicon expanding mechanism to remove the word ambiguity among different dimensions, and propose an algorithm for sentiment analysis on E-commerce reviews based on rules and a dimensional sentiment lexicon. We conduct experiments on a large-scale dataset involving over 28 million reviews, and compare our approach with the traditional way that does not consider dimensions of reviews. The results show that the multi-dimensional approach reaches a precision of 95.5% on average, and outperforms the traditional way in terms of precision, recall, and F-measure.
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
Feldman, R.: Techniques and applications for sentiment analysis. Communications of the ACM 56(4), 82–89 (2013)
Turney, R.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proc. of ACL, pp. 417–424 (2002)
Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Proc. of HLT-NAACL, pp. 804–812 (2010)
Fu, X., Liu, G., Guo, Y., Wang, Z.: Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems 37, 186–195 (2013)
Hogenboom, A., Boon, F., Frasincar, F.: A statistical approach to star rating classification of sentiment. In: Casillas, J., Martínez-López, F.J., Corchado, J.M. (eds.) Management of Intelligent Systems. AISC, vol. 171, pp. 251–260. Springer, Heidelberg (2012)
Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proc. of AAAI, pp. 755–760 (2004)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proc. of KDD, pp. 168–177 (2004)
Xu, J., Ding, Y., Wang, X., Wu, G.Y.: Identification of Chinese finance text using machine learning method. Proc. of SMC, 455–459 (2008)
Hu, M., Liu, B.: Opinion extraction and summarization on the web. In: Proc. of AAAI, pp. 1621–1624 (2006)
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proc. of WSDM, pp. 231–240 (2008)
Popescu, A., Etzioni, O.: Extracting product features and opinions from reviews. In: Natural Language Processing and Text Mining, pp. 9–28. Springer, London (2007)
Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In: Proc. of EMNLP, pp. 129–136 (2003)
Mullen, T., Collier, N.: Sentiment analysis using support vector machines with diverse Information sources. In: Proc. of EMNLP, pp. 412–418 (2004)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proc. of ACL, pp. 79–86 (2002)
Rushdi-Saleh, M., Martín-Valdivia, M.T., Ráez, A.M., López, L.A.U.: Experiments with SVM to classify opinions in different domains. Expert Systems with Applications 38(12), 14799–14804 (2011)
Zhang, Z., Ye, Q., Zhang, Z., Li, Y.: Sentiment classification of Internet restaurant reviews written in Cantonese. Expert Systems with Applications 38(6), 7674–7682 (2011)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)
Whitelaw, C., Garg, N., Argamon, S.: Using appraisal groups for sentiment analysis. In: Proc. of CIKM, pp. 625–631 (2005)
Liu, B.: Sentiment analysis and subjectivity. In: Handbook of natural language processing (2010)
Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: Proc. of EMNLP, pp. 1533–1541 (2009)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. of ICML, pp. 282–289 (2001)
Jakob, N., Gurevych, I.: Extracting opinion targets in a single-and cross-domain setting with conditional random fields. In: Proc. of EMNLP, pp. 1035–1045 (2010)
Fellbaum, C.: WordNet: An electronic lexical database, http://www.cogsci.princ
Kim, S.M., Hovy, E.: Determining the sentiment of opinions. Proc. of ACL 1367 (2004)
Esuli, A., Sebastiani, F.: Determining the semantic orientation of terms through gloss classification. In: Proc. of CIKM, pp. 617–624 (2005)
Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proc. of ACL, pp. 675–682 (2009)
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proc. of ACL, pp. 174–181 (1997)
Turney, P., Littman, M.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems 21(4), 315–346 (2003)
Ding, X., Liu, B.: Resolving object and attribute coreference in opinion mining. In: Proc. of ACL, pp. 268–276 (2010)
Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Computational Linguistics 37(1), 9–27 (2011)
Li, F., Pan, S.J., Jin, O., Yang, Q., Zhu, X.: Cross-domain co-extraction of sentiment and topic lexicons. In: Proc. of ACL, pp. 410–419 (2012)
Jin, P., Li, X., Chen, H., Yue, L.: CT-Rank: a time-aware ranking algorithm for web search. Journal of Convergence Information Technology 5(6), 99–111 (2010)
Zhao, X., Jin, P., Yue, L.: Automatic Temporal Expression Normalization with Reference Time Dynamic-Choosing. In: Proc. of COLING, pp. 1498–1506 (2010)
Zhang, Q., Jin, P., Lin, S., Yue, L.: Extracting Focused Locations for Web Pages. In: Wang, L., Jiang, J., Lu, J., Hong, L., Liu, B. (eds.) WAIM 2011. LNCS, vol. 7142, pp. 76–89. Springer, Heidelberg (2012)
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
Zheng, L., Jin, P., Zhao, J., Yue, L. (2014). Multi-dimensional Sentiment Analysis for Large-Scale E-commerce Reviews. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds) Database and Expert Systems Applications. DEXA 2014. Lecture Notes in Computer Science, vol 8645. Springer, Cham. https://doi.org/10.1007/978-3-319-10085-2_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-10085-2_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10084-5
Online ISBN: 978-3-319-10085-2
eBook Packages: Computer ScienceComputer Science (R0)