Algorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance

Sentiment analysis is a text classification task within the area of natural language processing whose objective is to detect the polarity (positive, negative or neutral) of an opinion given by a certain user. Knowing the opinion that a person has toward a product or service is of great help for deci...

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Autores:
Silva, Jesús
Sanchez Montero, Edgardo Rafael
Cabrera, Danelys
Chacon, Ramon
Vargas, Martin
Pineda Lezama, Omar Bonerge
Orellano, Nataly
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
spa
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7694
Acceso en línea:
https://hdl.handle.net/11323/7694
https://doi.org/10.1007/978-981-15-7234-0_90
https://repositorio.cuc.edu.co/
Palabra clave:
Analysis of polarity
Opinion mining
Supervised classification
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/7694
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repository_id_str
dc.title.spa.fl_str_mv Algorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance
title Algorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance
spellingShingle Algorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance
Analysis of polarity
Opinion mining
Supervised classification
title_short Algorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance
title_full Algorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance
title_fullStr Algorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance
title_full_unstemmed Algorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance
title_sort Algorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance
dc.creator.fl_str_mv Silva, Jesús
Sanchez Montero, Edgardo Rafael
Cabrera, Danelys
Chacon, Ramon
Vargas, Martin
Pineda Lezama, Omar Bonerge
Orellano, Nataly
dc.contributor.author.spa.fl_str_mv Silva, Jesús
Sanchez Montero, Edgardo Rafael
Cabrera, Danelys
Chacon, Ramon
Vargas, Martin
Pineda Lezama, Omar Bonerge
Orellano, Nataly
dc.subject.spa.fl_str_mv Analysis of polarity
Opinion mining
Supervised classification
topic Analysis of polarity
Opinion mining
Supervised classification
description Sentiment analysis is a text classification task within the area of natural language processing whose objective is to detect the polarity (positive, negative or neutral) of an opinion given by a certain user. Knowing the opinion that a person has toward a product or service is of great help for decision making, since it allows, among other things, potential consumers to verify the quality of the product or service before using it. This paper presents the results obtained from the automatic identification of the polarity of comments emitted by university students in a survey corresponding to the performance of their professors. In order to carry out the identification of the polarity of comments, a technique based on automatic learning is used, which initially makes a manual labeling of the comments and then these results allow to feed different learning algorithms in order to create the classification models that will be used to automatically label new comments, and thus determine their polarity as positive or negative.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-01-15T14:15:23Z
dc.date.available.none.fl_str_mv 2021-01-15T14:15:23Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-981-15-7234-0_90
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
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url https://hdl.handle.net/11323/7694
https://doi.org/10.1007/978-981-15-7234-0_90
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
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dc.relation.references.spa.fl_str_mv 1. 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, Association for Computational Linguistics, Denver, Colorado, pp 767–771
2. 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, Association for Computational Linguistics, San Diego, Californiapp, pp 282–286
3. 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, Association for Computational Linguistics, San Diego, California, pp 354–361
4. Deng ZH, Luo KH, Yu HL (2014) A study of supervised term weighting scheme for sentiment analysis. Expert Syst Appl 41:3506–3513
5. 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
6. Balaguer EV, Rosso P, Locoro A, Mascardi V (2010) Análisis de opiniones con ontologıas. Polibits 41:29–36
7. Sanzón YM, Vilariño D, Somodevilla MJ, Zepeda C, Tovar M (2015) Modelos para detectar la polaridad de los mensajes en redes sociales. Res Comput Sci 99:29–42
8. 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
9. Araújo M, Pereira A, Benevenuto F (2020) A comparative study of machine translation for multilingual sentence-level sentiment analysis. Inf Sci 512:1078–1102
10. 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
11. 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
12. Peng DL, Gu LZ, Sun B (2019) Sentiment analysis of Chinese product reviews based on models of SVM and LSTM. Comput Eng Softw 1:10
13. 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
14. Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20(1):53–65 [Online]. Disponible: http://dx.doi.org/10.1016/0377-0427(87)90125-7
15. 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, PA, USA, pp 173–180
16. 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
17. 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
18. He Q, Yang J, Lu G, Chen Z, Wang Y, Sato M, Qie X (2019) Analysis of the first positive polarity gigantic jet recorded near the Yellow Sea in mainland China. J Atmos Solar Terr Phys 190:6–15
19. Funahashi Y, Watanabe T, Kaibuchi K (2020) Advances in defining signaling networks for the establishment of neuronal polarity. Curr Opin Cell Biol 63:76–87
20. Das S, Das D, Kolya AK (2020) An approach for sentiment analysis of GST tweets using words popularity versus polarity generation. In: Computational intelligence in pattern recognition, Springer, Singapore, pp 69–80
dc.rights.spa.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
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dc.source.spa.fl_str_mv Advances in Intelligent Systems and Computing
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spelling Silva, JesúsSanchez Montero, Edgardo RafaelCabrera, DanelysChacon, RamonVargas, MartinPineda Lezama, Omar BonergeOrellano, Nataly2021-01-15T14:15:23Z2021-01-15T14:15:23Z2021https://hdl.handle.net/11323/7694https://doi.org/10.1007/978-981-15-7234-0_90Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Sentiment analysis is a text classification task within the area of natural language processing whose objective is to detect the polarity (positive, negative or neutral) of an opinion given by a certain user. Knowing the opinion that a person has toward a product or service is of great help for decision making, since it allows, among other things, potential consumers to verify the quality of the product or service before using it. This paper presents the results obtained from the automatic identification of the polarity of comments emitted by university students in a survey corresponding to the performance of their professors. In order to carry out the identification of the polarity of comments, a technique based on automatic learning is used, which initially makes a manual labeling of the comments and then these results allow to feed different learning algorithms in order to create the classification models that will be used to automatically label new comments, and thus determine their polarity as positive or negative.Silva, JesúsSanchez Montero, Edgardo RafaelCabrera, DanelysChacon, RamonVargas, MartinPineda Lezama, Omar BonergeOrellano, Natalyapplication/pdfspaCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_90Analysis of polarityOpinion miningSupervised classificationAlgorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers PerformanceArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. 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, Association for Computational Linguistics, Denver, Colorado, pp 767–7712. 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, Association for Computational Linguistics, San Diego, Californiapp, pp 282–2863. 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, Association for Computational Linguistics, San Diego, California, pp 354–3614. Deng ZH, Luo KH, Yu HL (2014) A study of supervised term weighting scheme for sentiment analysis. Expert Syst Appl 41:3506–35135. Peñalver I, Garcia F, Valencia R, Rodríguez MA, Moreno V, Fraga A, Sánchez JL (2014) Feature-based opinion mining through ontologies. Expert Syst Appl 41:5995–60086. Balaguer EV, Rosso P, Locoro A, Mascardi V (2010) Análisis de opiniones con ontologıas. Polibits 41:29–367. Sanzón YM, Vilariño D, Somodevilla MJ, Zepeda C, Tovar M (2015) Modelos para detectar la polaridad de los mensajes en redes sociales. Res Comput Sci 99:29–428. 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, Canada9. Araújo M, Pereira A, Benevenuto F (2020) A comparative study of machine translation for multilingual sentence-level sentiment analysis. Inf Sci 512:1078–110210. 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 12:2825–283011. 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 108–12212. Peng DL, Gu LZ, Sun B (2019) Sentiment analysis of Chinese product reviews based on models of SVM and LSTM. Comput Eng Softw 1:1013. 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/v9i47/10737114. Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20(1):53–65 [Online]. Disponible: http://dx.doi.org/10.1016/0377-0427(87)90125-715. 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, PA, USA, pp 173–18016. Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–120617. 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 155:575–58018. He Q, Yang J, Lu G, Chen Z, Wang Y, Sato M, Qie X (2019) Analysis of the first positive polarity gigantic jet recorded near the Yellow Sea in mainland China. J Atmos Solar Terr Phys 190:6–1519. Funahashi Y, Watanabe T, Kaibuchi K (2020) Advances in defining signaling networks for the establishment of neuronal polarity. Curr Opin Cell Biol 63:76–8720. Das S, Das D, Kolya AK (2020) An approach for sentiment analysis of GST tweets using words popularity versus polarity generation. In: Computational intelligence in pattern recognition, Springer, Singapore, pp 69–80PublicationORIGINALAlgorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance.pdfAlgorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance.pdfapplication/pdf68804https://repositorio.cuc.edu.co/bitstreams/e80469df-886a-4777-be8f-bdfda4d0ea01/downloade6a10bf66a761f943a3dba889d9cdd82MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/06e9f9bb-12ab-4986-b11d-fe320655d90f/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/5336d664-f094-442a-846e-65d7848bafb0/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILAlgorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance.pdf.jpgAlgorithm for Detecting Polarity of 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