Algorithm for detecting polarity of opinions in laptop and restaurant domains
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...
- Autores:
-
Silva, Jose
Varela Izquierdo, Noel
Cabrera, Danelys
Lezama, Omar
Varas, Jesus
Manco, Patricia
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7713
- Acceso en línea:
- https://hdl.handle.net/11323/7713
https://doi.org/10.1007/978-981-15-7907-3_33
https://repositorio.cuc.edu.co/
- Palabra clave:
- Opinion mining
Supervised learning
Natural Language Processing
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Algorithm for detecting polarity of opinions in laptop and restaurant domains |
| title |
Algorithm for detecting polarity of opinions in laptop and restaurant domains |
| spellingShingle |
Algorithm for detecting polarity of opinions in laptop and restaurant domains Opinion mining Supervised learning Natural Language Processing |
| title_short |
Algorithm for detecting polarity of opinions in laptop and restaurant domains |
| title_full |
Algorithm for detecting polarity of opinions in laptop and restaurant domains |
| title_fullStr |
Algorithm for detecting polarity of opinions in laptop and restaurant domains |
| title_full_unstemmed |
Algorithm for detecting polarity of opinions in laptop and restaurant domains |
| title_sort |
Algorithm for detecting polarity of opinions in laptop and restaurant domains |
| dc.creator.fl_str_mv |
Silva, Jose Varela Izquierdo, Noel Cabrera, Danelys Lezama, Omar Varas, Jesus Manco, Patricia |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jose Varela Izquierdo, Noel Cabrera, Danelys Lezama, Omar Varas, Jesus Manco, Patricia |
| dc.subject.spa.fl_str_mv |
Opinion mining Supervised learning Natural Language Processing |
| topic |
Opinion mining Supervised learning Natural Language Processing |
| description |
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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2021 |
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2021 |
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eng |
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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, Denver, Colorado, Association for Computational Linguistics, 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, San Diego, California, Association for Computational Linguistics, 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, San Diego, California, Association for Computational Linguistics, 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. Dragoni M, Federici M, Rexha A (2019) ReUS: a real-time unsupervised system for monitoring opinion streams. Cognit Comput 11(4):469–488 7 Viloria A., Acuña G.C., Franco D.J.A., Hernández-Palma H., Fuentes J.P., Rambal E.P. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-580 8 Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: 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 (2011) 9 Wilson, T., Wiebe, J., Hoffmann, P.: 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 (2005) 10 Bakliwal, A., Foster, J., van der Puil, J., OBrien, R., Tounsi, L., Hughes, M.: 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 (2013) 11 Khan F.H., Bashir S., Qamar U. Tom: Twitter opinion mining framework using hybrid classification scheme Decision Support Systems, 57 (2014), pp. 245-257 12 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, S.M., Eryigit, G.: 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 (2018) 13 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. Scikit-learn: Machine learning in Python Journal of Machine Learning Research, 12 (2011), pp. 2825-2830 14 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.: 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) 15 Viloria A., Gaitan-Angulo M. Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company Indian Journal of Science and Technology, 9 (47) (2018) doi:10.17485/ijst/2018/v9i47/107371. 16 P. Rousseeuw Silhouettes: A graphical aid to the interpretation and validation of cluster analysis J. Comput. Appl. Math., 20 (1) (1987), pp. 53-65 Nov.. [Online]. Disponible: http://dx.doi.org/10.1016/0377- 0427(87)90125-7. 17 F. Wilcoxon Individual comparisons by ranking methods Biometrics Bulletin, 1 (6) (1945), pp. 80-83 18 Martín-Wanton, Tamara, and Aurora Pons-Porrata. "Opinion polarity detection-using word sense disambiguation to determine the polarity of opinions." International Conference on Agents and Artificial Intelligence. Vol. 2. SCITEPRESS, 2010. 19 Hercig, T., Brychcín, T., Svoboda, L., Konkol, M.: 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 (2018) 20 Deng Z.H., Luo K.H., Yu H.L. A study of supervised term weighting scheme for sentiment analysis Expert Systems with Applications, 41 (2014), pp. 3506-3513 21 Peñalver I., Garcia F., Valencia R., Rodríguez M.A., Moreno V., Fraga A., Sánchez J.L. Feature-based opinion mining through ontologies Expert Systems with Applications, 41 (2014), pp. 5995-6008 22 Balaguer E.V., Rosso P., Locoro A., Mascardi V. Análisis de opiniones con ontologıas Polibits, 41 (2010), pp. 29-36 |
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Silva, JoseVarela Izquierdo, NoelCabrera, DanelysLezama, OmarVaras, JesusManco, Patricia2021-01-19T13:14:40Z2021-01-19T13:14:40Z2021https://hdl.handle.net/11323/7713https://doi.org/10.1007/978-981-15-7907-3_33Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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 EnglishSilva, JoseVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Cabrera, Danelys-will be generated-orcid-0000-0002-9486-9764-600Lezama, OmarVaras, JesusManco, Patriciaapplication/pdfengCorporación 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/10.1007/978-981-15-7907-3_33Opinion miningSupervised learningNatural Language ProcessingAlgorithm for detecting polarity of opinions in laptop and restaurant domainsArtículo 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, Denver, Colorado, Association for Computational Linguistics, 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, San Diego, California, Association for Computational Linguistics, 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, San Diego, California, Association for Computational Linguistics, 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. Dragoni M, Federici M, Rexha A (2019) ReUS: a real-time unsupervised system for monitoring opinion streams. Cognit Comput 11(4):469–4887 Viloria A., Acuña G.C., Franco D.J.A., Hernández-Palma H., Fuentes J.P., Rambal E.P. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-5808 Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: 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 (2011)9 Wilson, T., Wiebe, J., Hoffmann, P.: 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 (2005)10 Bakliwal, A., Foster, J., van der Puil, J., OBrien, R., Tounsi, L., Hughes, M.: 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 (2013)11 Khan F.H., Bashir S., Qamar U. Tom: Twitter opinion mining framework using hybrid classification scheme Decision Support Systems, 57 (2014), pp. 245-25712 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, S.M., Eryigit, G.: 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 (2018)13 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. Scikit-learn: Machine learning in Python Journal of Machine Learning Research, 12 (2011), pp. 2825-283014 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.: 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)15 Viloria A., Gaitan-Angulo M. Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company Indian Journal of Science and Technology, 9 (47) (2018) doi:10.17485/ijst/2018/v9i47/107371.16 P. Rousseeuw Silhouettes: A graphical aid to the interpretation and validation of cluster analysis J. Comput. Appl. Math., 20 (1) (1987), pp. 53-65 Nov.. [Online]. Disponible: http://dx.doi.org/10.1016/0377- 0427(87)90125-7.17 F. Wilcoxon Individual comparisons by ranking methods Biometrics Bulletin, 1 (6) (1945), pp. 80-8318 Martín-Wanton, Tamara, and Aurora Pons-Porrata. "Opinion polarity detection-using word sense disambiguation to determine the polarity of opinions." International Conference on Agents and Artificial Intelligence. Vol. 2. SCITEPRESS, 2010.19 Hercig, T., Brychcín, T., Svoboda, L., Konkol, M.: 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 (2018)20 Deng Z.H., Luo K.H., Yu H.L. A study of supervised term weighting scheme for sentiment analysis Expert Systems with Applications, 41 (2014), pp. 3506-351321 Peñalver I., Garcia F., Valencia R., Rodríguez M.A., Moreno V., Fraga A., Sánchez J.L. Feature-based opinion mining through ontologies Expert Systems with Applications, 41 (2014), pp. 5995-600822 Balaguer E.V., Rosso P., Locoro A., Mascardi V. 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