Abstract
The opinions and reviews analysis has been an important part of research as people around the globe are using these data for making decision for the purchase of services or product. The analysis and summarization of this has also benefited the service provider and manufacturer for the updation. The analysis methodology requires the updated domain information for mapping the opinions and reviews to the particular domain entity; if it is not updated, the analysis will not be accurate as these opinions that belong to the domain are left unmatched, as they were not updated in the domain information. This paper deals with the design and development of Novel Mapper algorithm that will be used for the predication of the particular entity and will add it to the particular domain information. The proposed novel mapper algorithm uses the Levenshtein Distance for calculating the distance between the matched and unmatched entity. The proposed methodology is compared with different existing algorithms for calculating the accuracy and efficiency of the proposed model.
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Data Availability
Two different datasets are used in this research: Tripadvisor, Available online: http://www.tripadvisor.com (accessed on 1 November 2021), Zenodo, Available online: https://zenodo.org/record/1219899#.YWaztBpBxPY (accessed on 1 November 2021)
References
Jacobs PS (1994) Text-based systems and information management: artificial intelligence confronts matters of scale. In: Jacobs, P.S Proceedings sixth international conference on tools with artificial intelligence. Doi: https://doi.org/10.1109/TAI.1994.346487.
Liu C-L, Jaeger S, Nakagawa M. Online recognition of chinese characters: the State-of-the-Art. IEEE Trans Pattern Anal Mach Intell. 2004;26:198–213. https://doi.org/10.1109/TPAMI.2004.1262182.
Soo-Min Kim, Eduard Hovy. 2006. Automatic Identification of Pro and Con Reasons in Online Reviews. In Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages 483–490, Sydney, Australia. Association for Computational Linguistics.
Wang, Hongning & Lu, Yue & Zhai, Chengxiang. (2010). Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 783–792. https://doi.org/10.1145/1835804.1835903.
Ghanbari A, Hadavandi E, Abbasian-Naghneh S. Comparison of Artificial intelligence based techniques for short term load forecasting. Fifth Int Conf Bus Intel Financ Eng. 2010;2012:6–10. https://doi.org/10.1109/BIFE.2010.12.
Zhu J, Zhang C, Ma MY. Multi-aspect rating inference with aspect-based segmentation in IEEE transactions on affective computing. Fourth Quarter. 2012;3:469–81. https://doi.org/10.1109/T-AFFC.2012.18.
Liu C-L, Hsaio W-H, Lee C-H, Lu G-C, Jou E. Movie rating and review summarization in mobile environment in IEEE transactions on systems, man, and cybernetics. Part C Appl Rev. 2012;42(3):397–407. https://doi.org/10.1109/TSMCC.2011.2136334.
Xueke Xu, Xueqi C, Songbo T, Yue L, Shen H. Aspect-level opinion mining of online customer reviews. Commun China. 2013;10:25–41. https://doi.org/10.1109/CC.2013.6488828.
McAuley, Julian and Leskovec, (2013). Hidden factors and hidden topics: Understanding rating dimensions with review text. In: 2013—Proceedings of the 7th ACM Conference on Recommender Systems. p. 165–172. https://doi.org/10.1145/2507157.2507163.
Grimmer J, Stewart B. Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Polit Anal. 2013;21:267–97. https://doi.org/10.1093/pan/mps028.
Zhang M-L, Zhou Z-H. A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng. 2014;26(8):1819–37. https://doi.org/10.1109/TKDE.2013.39.
Wu DD, Zheng L, Olson DL. A decision support approach for online stock forum sentiment analysis. IEEE Transact Syst Man Cyber: Syst. 2014;44(8):1077–87. https://doi.org/10.1109/TSMC.2013.2295353.
Susan M, David S, Ermira Z "What’s “Funny” about Technology Adoption? Humorous Appropriation of Online Review Platforms" (2016). ICIS 2016 Proceedings. 10.https://aisel.aisnet.org/icis2016/ITImplementation/Presentations/10
Kansal H, Toshniwal D. Aspect based summarization of context dependent opinion words. Proce Comp Sci. 2014;35:166–75. https://doi.org/10.1016/j.procs.2014.08.096.
Zhang X, Cui L, Wang Y. commtrust: computing multi-dimensional trust by mining e-commerce feedback comments. IEEE Trans Knowl Data Eng. 2014;26(7):1631–43. https://doi.org/10.1109/TKDE.2013.177.
Ordenes FV, Theodoulidis B, Burton J, Gruber T, Zaki M. Analyzing customer experience feedback using text mining: a linguistics-based approach. J Serv Res. 2014;17(3):278–95. https://doi.org/10.1177/1094670514524625.
Liu S, Cheng X, Li F, Li F. TASC: topic-adaptive sentiment classification on dynamic tweets. IEEE Trans Knowled Data Eng. 2015;27:1696–709. https://doi.org/10.1109/TKDE.2014.2382600.
Johnson A, Ghassemi M, Nemati S, Niehaus K, Clifton D, Clifford G. Machine learning and decision support in critical care. Proc IEEE. 2016;104:444–66. https://doi.org/10.1109/JPROC.2015.2501978.
Sun, Xiao & Sun, Chongyuan & Ren, Fuji & Tian, Fang & Wang, Kunxia. Emotional element detection and tendency judgment based on mixed model with deep features. 2016; https://doi.org/10.1109/ICIS.2016.7550779.
Agrawal P, Agrawal A. Opinion analysis using domain ontology for implementing natural language based feedback system. Int J Infor Technol Comp Sci. 2014;6:61–9. https://doi.org/10.5815/ijitcs.2014.03.08.
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Agrawal, P.K. A Novel Mapper Machine Learning Algorithm for Semantic Domain Mapping for Domain Database Updation. SN COMPUT. SCI. 4, 536 (2023). https://doi.org/10.1007/s42979-023-02036-0
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DOI: https://doi.org/10.1007/s42979-023-02036-0