Comparative analysis between different automatic learning environments for sentiment analysis

dc.creatoramelec, viloria
dc.creatorVarela Izquierdo, Noel
dc.creatorVargas, Jesús
dc.creatorPineda, Omar
dc.date2020-11-12T17:37:01Z
dc.date2020-11-12T17:37:01Z
dc.date2020
dc.date2021-06-19
dc.date.accessioned2023-10-03T19:42:44Z
dc.date.available2023-10-03T19:42:44Z
dc.identifier2194-5357
dc.identifierhttps://hdl.handle.net/11323/7279
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9171677
dc.descriptionSentiment Analysis is a branch of Natural Language Processing in which an emotion is identified through a sentence, phrase or written expression on the Internet, allowing the monitoring of opinions on different topics discussed on the Web. The study discussed in this paper analyzed phrases or sentences written in Spanish and English expressing opinions about the service of Restaurants and opinions written in the English language about Laptops. Experiments were carried out using 3 automatic classifiers: Support Vector Machine (SVM), Naïve Bayes and Multinomial Naïve Bayes, each one being tested with the three data sets in the Weka automatic learning software and in Python, in order to make a comparison of results between these two tools
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
dc.relationZhang, Z., Ye, Q., Zhang, Z., Li, Y.: Sentiment classification of internet restaurant reviews written in cantonese. Expert Syst. Appl. 38(6), 7674–7682 (2011)
dc.relationBillyan, B., Sarno, R., Sungkono, K.R., Tangkawarow, I.R.: Fuzzy K-nearest neighbor for restaurants business sentiment analysis on TripAdvisor. In: 2019 International Conference on Information and Communications Technology (ICOIACT), pp. 543–548. IEEE, July 2019
dc.relationBuitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., Varoquaux, G.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)
dc.relationLaksono, R.A., Sungkono, K.R., Sarno, R., Wahyuni, C.S.: Sentiment analysis of restaurant customer reviews on TripAdvisor using Naïve Bayes. In: 2019 12th International Conference on Information & Communication Technology and System (ICTS), pp. 49–54. IEEE, July 2019
dc.relationSingh, S., Saikia, L.P.: A comparative analysis of text classification algorithms for ambiguity detection in requirement engineering document using WEKA. In: ICT Analysis and Applications, pp. 345–354. Springer, Singapore (2020)
dc.relationKumar, A., Jaiswal, A.: Systematic literature review of sentiment analysis on Twitter using soft computing techniques. Concurr. Computa.: Pract. Exp. 32(1), e5107 (2020)
dc.relationMulay, S.A., Joshi, S.J., Shaha, M.R., Vibhute, H.V., Panaskar, M.P.: Sentiment analysis and opinion mining with social networking for predicting box office collection of movie. Int. J. Emerg. Res. Manag. Technol. 5(1), 74–79 (2016)
dc.relationLiu, S., Lee, I.: Email sentiment analysis through k-means labeling and support vector machine classification. Cybern. Syst. 49(3), 181–199 (2018)
dc.relationPontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., Hoste, V., Apidianaki, M., Tannier, X., Loukachevitch, N., Kotelnikov, E., Bel, N., Jimenez-Zafra, S.M., Eryigit, G.: Semeval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30. Association for Computational Linguistics, San Diego, June 2016. http://www.aclweb.org/anthology/S16-1002
dc.relationAhmad, M., Aftab, S., Bashir, M.S., Hameed, N., Ali, I., Nawaz, Z.: SVM optimization for sentiment analysis. Int. J. Adv. Comput. Sci. Appl. 9(4), 393–398 (2018)
dc.relationRennie, J.D., Shih, L., Teevan, J., Karger, D.R., et al.: Tackling the poor assumptions of Naive Bayes text classifiers. In: Proceedings of the Twentieth International Conference on Machine Learning, Washington DC, vol. 3, pp. 616–623 (2003)
dc.relationAhmad, M., Aftab, S., Ali, I.: Sentiment analysis of tweets using SVM. Int. J. Comput. Appl. 177(5), 25–29 (2017)
dc.relationDucange, P., Fazzolari, M., Petrocchi, M., Vecchio, M.: An effective decision support system for social media listening based on cross-source sentiment analysis models. Eng. Appl. Artif. Intell. 78, 71–85 (2019)
dc.relationIqbal, F., Hashmi, J.M., Fung, B.C., Batool, R., Khattak, A.M., Aleem, S., Hung, P.C.: A hybrid framework for sentiment analysis using genetic algorithm based feature reduction. IEEE Access 7, 14637–14652 (2019)
dc.relationSilva, J., Varela, N., Ovallos-Gazabon, D., Palma, H.H., Cazallo-Antunez, A., Bilbao, O.R., Llinás, N.O., Lezama, O.B.P.: Data mining and social network analysis on Twitter. In: International Conference on Communication, Computing and Electronics Systems, pp. 401–408. Springer, Singapore (2020)
dc.relationSilva, J., Naveda, A.S., Suarez, R.G., Palma, H.H., Núñez, W.N.: Method for collecting relevant topics from Twitter supported by big data. In: Journal of Physics: Conference Series, vol. 1432, no. 1, p. 012094. IOP Publishing, January 2020
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.rightshttp://purl.org/coar/access_right/c_14cb
dc.sourceAdvances in Intelligent Systems and Computing
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089721615&doi=10.1007%2f978-3-030-53036-5_14&partnerID=40&md5=a16c1d0bc02ec0dacfd7ced4d831e746
dc.subjectAutomatic learning
dc.subjectComparative analysis
dc.subjectSentiment analysis
dc.titleComparative analysis between different automatic learning environments for sentiment analysis
dc.typePre-Publicación
dc.typehttp://purl.org/coar/resource_type/c_816b
dc.typeText
dc.typeinfo:eu-repo/semantics/preprint
dc.typeinfo:eu-repo/semantics/draft
dc.typehttp://purl.org/redcol/resource_type/ARTOTR
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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