The week's pick
Random Articles
Reseach Article
Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 144 - Number 2 |
| Year of Publication: 2016 |
| Authors: Mangal Singh, Tabrez Nafis, Neel Mani |
10.5120/ijca2016910112
|
Mangal Singh, Tabrez Nafis, Neel Mani . Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews. International Journal of Computer Applications. 144, 2 ( Jun 2016), 16-19. DOI=10.5120/ijca2016910112
Abstract
Sentiment analysis and classification is a prominent research topic in academics as well as in industrial field. Since each customer reviews text always state emotion about a target domain, sentiment classification is a highly domain dependent task and present study considered the reviews from heterogeneous domains. Generally researchers classify the customer review with positive, negative and neutral sentiments but a positive review can be highly positive and a negative review can be highly negative, so sentiment analysis about a review can be more effective if a sentiment scale is also defined for such greater degree of positivity or negativity. We defined a framework to classify heterogeneous product reviews with degree of polarity on a sentiment scale of range -2 to 2. For each review, an intermediate form is calculated using sentiment vectors which is further processed to calculate the sentiment polarity magnitude and similarity of reviews.
References
- Wu, F. and Huang, Y. 2015. "Collaborative Multi-domain Sentiment Classification," Data Mining (ICDM), IEEE International Conference on, Atlantic City, NJ, pp. 459-468.
- Bisio, F., Gastaldo, P., Peretti, C., Zunino, R. and Cambria, E. 2013. "Data intensive review mining for sentiment classification across heterogeneous domains," Advances in Social Networks Analysis and Mining (ASONAM), IEEE/ACM International Conference on, Niagara Falls, ON, pp. 1061-1067.
- Bollegala, D., Weir D. and Carroll, J. 2013. "Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus," in IEEE Transactions on Knowledge and Data Engineering, 25(8):1719-1731.
- Glorot, X., Bordes, A. and Bengio, Y. 2011. “Domain adaptation for large scale sentiment classification: A deep learning approach”. In proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 513-520
- Gokulakrishnan, B., Priyanthan, P., Ragavan, T., Prasath, N. and Perera, A. 2012. "Opinion mining and sentiment analysis on a Twitter data stream," Advances in ICT for Emerging Regions (ICTer), International Conference on, Colombo, pp. 182-188
- Nie, P., Zhao, X., Yu, L., Wang, C. and Zhang, Y. 2015. “Social Emotion Analysis System for Online News”. In proceedings of 12th Web Information System and Application Conference.
- Hu, M. and Liu, B. 2004. Mining and summarizing customer reviews. In KDD, ACM, pp 168-177.
- Pang, B. and Lee, L. 2008. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2):1-135.
- Ren, F. and Wu, Y. 2013. Predicting user-topic opinions in twitter with social and topical context. IEEE Transactions on Affective Computing, 4(4):412–424.
- Lin, D. 1998. An information-theoretic definition of similarity. In Proceedings of 15th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA, pp. 296–304.
- Zhou, Y. 2015. “The analysis of online users’ emotions based on data mining”, 3rd International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2015).
- Madylova, A. and Oguducu, S. G. 2009. “A Taxonomy based Semantic Similarity of Documents using the Cosine Measure,” IEEE.
- Tang, D., Wei, F,. Qin, B., Yang, N., Liu, T. and Zhou, M. 2016. "Sentiment Embeddings with Applications to Sentiment Analysis," in IEEE Transactions on Knowledge and Data Engineering, 28(2): 496-509.
- Glorot, X., Bordes, A. and Bengio, Y. 2011. “Domain adaptation for large scale sentiment classification: A deep learning approach,” ICML.
- Lin, L., Jianxin, L., Zhang, W. and Sun, Y. 2014. Opinion Mining and Sentiment Analysis in Social Networks: A Retweeting Structure-Aware Approach. In proceedings of IEEE/ACM 7th international Conference on Utility and Cloud Computing, Washington, DC, USA,
- Blitzer, J., Dredze, M. and Pereira, F. 2007. “Biographies, Bollywoo, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification.” ACL, 7: 440-447.
- Pang, B., Lee, L. and Vaithyanathan, S. 2002. "Thumbs up?: sentiment classification using machine learning techniques." ACL, pp.79-86.
- Chun-Han Chu, Apoorva Honnegowda Roopa, Yung-Chun Chan, and Wen-Lian Hsu. 2015. "Constructing sentiment sensitive vectors for word polarity classification." In proceedings of Conference on Technologies and Applications of Artificial Intelligence, pp. 252-259.
- Balla-Muller Nora, Lemnaru, C. and Potoles, R. 2010. “Semi-Supervised Learning with Lexical Knowledge for Opinion Mining”. In Proceedings of IEEE 6th International Conference on Intelligent Computer Communication and Processing, pp. 19-25.
- George A. Miller. 1995. WordNet: A Lexical Database for English. Communications of the ACM, 38(11): 39-41.
- Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press. 422 p.
- McAuley, J., Targett, C., Shi, Q. and Hengel, A. van den. 2015. “Image-Based Recommendations on Styles and Substitutes”. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 43-52.
- McAuley, J., Pandey, R. and Leskovec, J. 2015. “Inferring networks of substitutable and complementary products”. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794.
Index Terms
Keywords