Abstract
Recently, opinion mining has become a focus in the field of natural language processing and web mining. Due to the massive amount of users’ reviews on the web about some entities or services, opinion mining is appeared to track users’ emotions and feelings. Sentiment analysis is a synonym to opinion mining. Feature extraction is an important task in the sentiment analysis process. So, in this paper, a novel model is proposed to extract the most related features to a product from customer reviews using semantic similarity. Wordnet taxonomy and Stanford Part of Speech (POS) tagger are used in the feature extraction process. The extracted features are very important to generate a meaningful feature based product reviews summery which helps the customers to make a decision. The experiments are performed on three different datasets. The proposed model achieves promising results in terms of Precision, Recall and F-measure performance measures.
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References
Arunkarthi, A., Gandhi, M.: Aspect-based opinion mining from online reviews. Res. J. Pharm. Biol. Chem. Sci. 7(3), 494–500 (2016)
Khan, K., Baharudin, B., Khan, A.: Identifying product features from customer reviews using hybrid patterns. Int. Arab J. Inform. Technol. 11(3), 281–286 (2014)
Htay, S.S., Lynn, K.T.: Extracting product features and opinion words using pattern knowledge in customer reviews. Sci. World J. 2013(3), 1–5 (2013)
Asghar, M.Z., Khan, A., Zahra, S.R., Ahmad, S., Kundi, F.M.: Aspect-based opinion mining framework using heuristic patterns. Cluster Comput. 20, 1–19 (2017)
Maharani, W., Widyantoro, D.H., Khodra, M.L.: Aspect extraction in customer reviews using syntactic pattern. Proc. Comput. Sci. 59(Iccsci), 244–253 (2015)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)
Liu, Q., Liu, B., Zhang, Y., Kim, D.S., Gao, Z.: Improving opinion aspect extraction using semantic similarity and aspect associations. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI-16, pp. 2986–2992 (2016)
Asgarian, E.: Subjective data mining on the web. Ferdowsi University of Mashhad (2014)
Vivekanandan, K., Aravindan, J.S.: Aspect-based opinion mining: a survey. Int. J. Comput. Appl. 106(3), 0975–8887 (2014)
Liu, Q., Gao, Z., Liu, B., Zhang, Y.: A logic programming approach to aspect extraction in opinion mining. In: IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology, (IAT) (2013)
Gupta, N., Kumar, P., Gupta, R.: Automated extraction of product attributes from reviews. CS 224N Final Project (2009)
Mishra, N., Jha, C.K.: Classification of opinion mining techniques. Int. J. Comput. Appl. 56(13), 0975–8887 (2012)
Sharma, R., Nigam, S., Jain, R.: Mining of product reviews at aspect level. Int. J. Found. Comput. Sci. Technol. 4(3), 87–95 (2014)
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Aboelela, E.M., Gad, W., Ismail, R. (2020). Feature Extraction Using Semantic Similarity. In: Hassanien, A., Shaalan, K., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_8
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DOI: https://doi.org/10.1007/978-3-030-31129-2_8
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