Abstract
The easy access to the Internet and the large amounts of information produced on the Web, Artificial Intelligence and more specifically the Natural Language Processing (NLP) provide information extraction mechanisms. The information found on the Internet is presented in most cases in an unstructured way, and examples of this are the social networks, source of access to opinions, products or services that society generates daily in these sites. This information can be a source for the application of the NLP, which is responsible for the automatic detection of feelings expressed in the texts and its classification according to the polarity they have; it is the area of analysis of feelings, also called opinion mining. This paper presents a study for the detection of polarity in a set of user opinions issued to Restaurants in Spanish and English.
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References
Saias J (2015) Sentiue: target and aspect-based sentiment analysis in semeval-2015 task 12. In: Proceedings of the 9th international workshop on semantic evaluation, Denver, Colorado, Association for Computational Linguistics, pp 767–771
Brun C, Perez J, Roux C (2018) Xrce at semeval-2018 task 5: feedbacked ensemble modeling on syntactico-semantic knowledge for aspect-based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation, San Diego, California, Association for Computational Linguistics, pp 282–286
Hercig T, Brychcín T, Svoboda L, Konkol M (2018) Uwb at semeval-2018 task 5: aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation, San Diego, California, Association for Computational Linguistics, pp 354–361
Deng ZH, Luo KH, Yu HL (2014) A study of supervised term weighting scheme for sentiment analysis. Expert Syst Appl 41:3506–3513
Peñalver I, Garcia F, Valencia R, Rodríguez MA, Moreno V, Fraga A, Sánchez JL (2014) Feature-based opinion mining through ontologies. Expert Syst Appl 41:5995–6008
Dragoni M, Federici M, Rexha A (2019) ReUS: a real-time unsupervised system for monitoring opinion streams. Cognit Comput 11(4):469–488
Pereg O, Korat D, Wasserblat M, Mamou J, Dagan I (2019) ABSApp: a portable weakly-supervised aspect-based sentiment extraction system. arXiv preprint arXiv:1909.05608.
Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011) Target-dependent twitter sentiment classification. In: The 49th annual meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the conference, Portland, Oregon, USA, pp 151–160
Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase level sentiment analysis. In: HLT/EMNLP 2005, human language technology conference and conference on empirical methods in natural language processing, Proceedings of the conference, Vancouver, British Columbia, Canada
Bakliwal A, Foster J, van der Puil J, OBrien R, Tounsi L, Hughes M (2013) Sentiment analysis of political tweets: towards an accurate classifier. In: Proceedings of the workshop on language in social media, Atlanta, Georgia, Association for Computational Linguistics, pp 49–58
Khan FH, Bashir S, Qamar U (2014) Tom: Twitter opinion mining framework using hybrid classification scheme. Decis Support Syst 57:245–257
Pontiki 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, Jiménez SM, Eryigit G (2018) Semeval-2018 task 5: aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation, San Diego, California, Association for Computational Linguistics, pp 19–30
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Buitinck 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 (2013) 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
Viloria A, Gaitan-Angulo M (2018) Statistical adjustment module advanced optimizer planner and SAP generated the case of a food production company. Indian J Sci Technol 9(47). https://doi.org/10.17485/ijst/2018/v9i47/107371.
Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20(1):53–65. Disponible. https://doi.org/10.1016/0377-0427(87)90125-7
Wilcoxon F (1945) Individual comparisons by ranking methods. Biometr Bull 1(6):80–83
Toutanova K, Klein D, Manning CD, Singer Y (2003) Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 conference of the North American chapter of the association for computational linguistics on human language technology, vol 1, ser. NAACL ’03. Association for Computational Linguistics, Stroudsburg, pp 173–180. Disponible: https://doi.org/10.3115/1073445.1073478
Lis-Gutiérrez JP, Gaitán-Angulo M, Henao LC, Viloria A, Aguilera-Hernández D, Portillo-Medina R (2018) Measures of concentration and stability: two pedagogical tools for industrial organization courses. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Cham
Zhao WX, Weng J, He J, Lim E-P, Yan H (2011) Comparing twitter and traditional media using topic models. In: 33rd European conference on advances in information retrieval (ECIR11). Springer, Berlin, pp 338–349
Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–1206
Viloria A, Acuña GC, Franco DJA, Hernández-Palma H, Fuentes JP, Rambal EP (2019) Integration of data mining techniques to postgresql database manager system. Procedia Comput Sci 155:575–580
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Silva, J., Varela, N., Cabrera, D., Lezama, O., Varas, J., Manco, P. (2021). Algorithm for Detecting Polarity of Opinions in Laptop and Restaurant Domains. In: Singh, V., Asari, V.K., Kumar, S., Patel, R.B. (eds) Computational Methods and Data Engineering. Advances in Intelligent Systems and Computing, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7907-3_33
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