Abstract
Comparative opinion mining has received considerable attention from both individuals and business companies for analyzing public feedback about the competing products. The user reviews about the different products posted on social media sites, provide an opportunity to opinion mining researchers to develop applications capable of performing comparative opinion mining on different products. Therefore, it is an important task of investigating the applicability of different supervised machine learning algorithms with respect to classification of comparative reviews. In this work different machine learning algorithms are applied for performing multi-class classification of comparative user reviews into different classes. The results show that Random Forest outperforms amongst all other classifiers used in the research.
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References
Asghar, M.Z., Kundi, F.M., Ahmad, S., Khan, A., Khan, F.: T-SAF: Twitter sentiment analysis framework using a hybrid classification scheme. Expert Syst. 35(1), e12233 (2018)
Khan, A., Asghar, M.Z., Ahmad, H., Kundi, F.M., Ismail, S.: A rule-based sentiment classification framework for health reviews on mobile social media. J. Med. Imaging Health Inform. 7(6), 1445–1453 (2017)
Khan, A.U.R., Khan, M., Khan, M.B.: Naïve multi-label classification of YouTube comments using comparative opinion mining. Procedia Comput. Sci. 82(16), 57–64 (2016)
Asghar, M.Z., Khan, A., Zahra, S.R., Ahmad, S., Kundi, F.M.: Aspect-based opinion mining framework using heuristic patterns. Cluster Comput. 20(1), 1–19 (2017)
Bach, N.X., Van, P.D., Tai, N.D., Phuong, T.M.: Mining Vietnamese comparative sentences for sentiment analysis. In: Merialdo, B., Nguyen, L.M., Li, D.D., Duong, D.A., Tojo, S. (eds.) CONFERENCE 2015, KSE, vol. 7, pp. 162–167. IEEE, Ho Chi Minh (2015)
Jin, J., Ji, P., Gu, R.: Identifying comparative customer requirements from product online reviews for competitor analysis. Eng. Appl. Artif. Intell. 49(C), 61–73 (2016)
Varathan, K.D., Giachanou, A., Crestani, F.: Comparative opinion mining: a review. J. Assoc. Inf. Sci. Technol. 68(4), 811–829 (2017)
Ganapathibhotla, M., Liu, B.: Mining opinions in comparative sentences. In: Scott, D., Uszkoreit, H. (eds.) CONFERENCE 2008, COLING, vol. 1, pp. 241–248. Association for Computational Linguistics, Manchester (2008)
Ejaz, A., Turabee, Z., Rahim, M., Khoja, S.: Opinion mining approaches on Amazon product reviews: a comparative study. In: Mahmood, T., Rauf, I., Khoja, S., Ghani, S. (eds.) CONFERENCE 2017, ICICT, vol. 17, pp. 173–179. IEEE, Karachi (2017)
Vargas, D.S., Moreira, V.: Identifying sentiment-based contradictions. J. Inf. Data Manag. 8(3), 242 (2017)
Liang, X., Qu, Y., Ma, G.: Research on contrastive viewpoint summarization for opinionated texts. J. Interconnect. Netw. 14(03), 1360003 (2013)
Hetland, M.: Python and the Web. Beginning Python: From Novice to Professional, 2nd edn. Apress, New York (2005)
Asghar, M.Z., Khan, A., Khan, F., Kundi, F.M.: RIFT: a rule induction framework for Twitter sentiment analysis. Arab. J. Sci. Eng. 43(2), 857–877 (2018)
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Khan, A. et al. (2020). Sentiment Classification of User Reviews Using Supervised Learning Techniques with Comparative Opinion Mining Perspective. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_3
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DOI: https://doi.org/10.1007/978-3-030-17798-0_3
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