Call for Paper - September 2022 Edition
IJCA solicits original research papers for the September 2022 Edition. Last date of manuscript submission is August 22, 2022. Read More

Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Mangal Singh, Tabrez Nafis, Neel Mani

Mangal Singh, Tabrez Nafis and Neel Mani. Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews. International Journal of Computer Applications 144(2):16-19, June 2016. BibTeX

	author = {Mangal Singh and Tabrez Nafis and Neel Mani},
	title = {Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {144},
	number = {2},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {16-19},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2016910112},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


  1. Wu, F. and Huang, Y. 2015. "Collaborative Multi-domain Sentiment Classification," Data Mining (ICDM), IEEE International Conference on, Atlantic City, NJ, pp. 459-468.
  2. 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.
  3. 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.
  4. 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
  5. 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
  6. 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.
  7. Hu, M. and Liu, B. 2004. Mining and summarizing customer reviews. In KDD, ACM, pp 168-177.
  8. Pang, B. and Lee, L. 2008. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2):1-135.
  9. 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.
  10. 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.
  11. 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).
  12. Madylova, A. and Oguducu, S. G. 2009. “A Taxonomy based Semantic Similarity of Documents using the Cosine Measure,” IEEE.
  13. 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.
  14. Glorot, X., Bordes, A. and Bengio, Y. 2011. “Domain adaptation for large scale sentiment classification: A deep learning approach,” ICML.
  15. 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,
  16. Blitzer, J., Dredze, M. and Pereira, F. 2007. “Biographies, Bollywoo, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification.” ACL, 7: 440-447.
  17. Pang, B., Lee, L. and Vaithyanathan, S. 2002. "Thumbs up?: sentiment classification using machine learning techniques." ACL, pp.79-86.
  18. 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.
  19. 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.
  20. George A. Miller. 1995. WordNet: A Lexical Database for English. Communications of the ACM, 38(11): 39-41.
  21. Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press. 422 p.
  22. 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.
  23. 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.


Sentiment Vector, Intermediate Form, Sentiment Polarity Magnitude.