Unsupervised model for aspect-based sentiment analysis in spanish

This paper presents an unsupervised model for Aspect-Based Sentiment Analysis in Spanish language, which automatically extracts the aspects of opinion and determines its associated polarity. The model uses ontologies for the detection of explicit and implicit aspects, and machine learning without su...

Full description

Autores:
Henríquez Miranda, Carlos
Briceño Díaz, Freddy
Salcedo, Dixon
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5648
Acceso en línea:
https://hdl.handle.net/11323/5648
https://repositorio.cuc.edu.co/
Palabra clave:
Aspect-based
Ontology
Sentiment analysis
Unsupervised machine learning
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_3cd1d4d4727cd239f88f5d6961cbedf8
oai_identifier_str oai:repositorio.cuc.edu.co:11323/5648
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Unsupervised model for aspect-based sentiment analysis in spanish
dc.title.translated.spa.fl_str_mv Modelo no supervisado para análisis de sentimiento basado en aspectos in español
title Unsupervised model for aspect-based sentiment analysis in spanish
spellingShingle Unsupervised model for aspect-based sentiment analysis in spanish
Aspect-based
Ontology
Sentiment analysis
Unsupervised machine learning
title_short Unsupervised model for aspect-based sentiment analysis in spanish
title_full Unsupervised model for aspect-based sentiment analysis in spanish
title_fullStr Unsupervised model for aspect-based sentiment analysis in spanish
title_full_unstemmed Unsupervised model for aspect-based sentiment analysis in spanish
title_sort Unsupervised model for aspect-based sentiment analysis in spanish
dc.creator.fl_str_mv Henríquez Miranda, Carlos
Briceño Díaz, Freddy
Salcedo, Dixon
dc.contributor.author.spa.fl_str_mv Henríquez Miranda, Carlos
Briceño Díaz, Freddy
Salcedo, Dixon
dc.subject.spa.fl_str_mv Aspect-based
Ontology
Sentiment analysis
Unsupervised machine learning
topic Aspect-based
Ontology
Sentiment analysis
Unsupervised machine learning
description This paper presents an unsupervised model for Aspect-Based Sentiment Analysis in Spanish language, which automatically extracts the aspects of opinion and determines its associated polarity. The model uses ontologies for the detection of explicit and implicit aspects, and machine learning without supervision to determine the polarity of a grammatical structure in Spanish. The unsupervised approach used, allows implementing a system quickly scalable to any language or domain. The experimental work was carried out using the dataset used in Semeval 2016 for Task 5 corresponding to Sentence-level ABSA. The implemented system obtained a 73.07 F1 value in the extraction of aspects and 84.8% accuracy in the sentiment classification. The system obtained the best results of all systems participating in the competition in the three issues mentioned above.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-11-13T20:13:45Z
dc.date.available.none.fl_str_mv 2019-11-13T20:13:45Z
dc.date.issued.none.fl_str_mv 2019-08-12
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/preprint
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ARTOTR
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_816b
status_str acceptedVersion
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/5648
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/5648
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv Alvarez-López, T., Juncal-Martinez, J., FernándezGavilanes, M., Costa-Montenegro, E., & GonzálezCastano, F. J. (2016). Gti at semeval-2016 task 5: Svm and crf for aspect detection and unsupervised aspectbased sentiment analysis. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016) (pp. 306–311).
Antònia Martí, M., Taulé, M., Teresa, M., Salud, M.-V., & Jiménez-Zafra, M. (2016). La negación en español: análisis y tipología de patrones de negación * Negation in Spanish: analysis and typology of negation patterns. Procesamiento Del Lenguaje Natural, (57), 41–48.
Cadilhac, A., Benamara, F., & Aussenac-Gilles, N. (2010). Ontolexical resources for feature based opinion mining : a case-study, 77–86.
Chaves, M., Larissa Freitas, & Renata Vieira. (2012). Hontology: a multilingual ontology for the accommodation sector in the tourism industry. In CTIC/STI - Comunicações a Conferências. Retrieved from http://hdl.handle.net/10884/654
Church, K. W., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22–29.
Cruz, F. L., Troyano, J. A., Enriquez, F., & Ortega Universidad de Sevilla AvReina Mercedes, J. (2008). Clasificación de documentos basada en la opinión: experimentos con un corpus de críticas de cine en español Experiments in sentiment classification of movie reviews in Spanish. Procesamiento de Lenguaje Natural, 41, 73–80.
De Freitas, L. A., & Vieira, R. (2013). Ontology-based Feature Level Opinion Mining for Portuguese Reviews. In Proceedings of the 22nd International Conference on World Wide Web. ACM, (pp. 367–370).
Dey, L., & Haque, S. M. (2008). Opinion mining from noisy text data. In Proceedings of the second workshop on Analytics for noisy unstructured text data.
Dubiau, L., & Ale, J. M. (2013). Análisis de Sentimientos sobre un Corpus en Español: Experimentación con un Caso de Estudio. In Argentine Symposium on Arti_cial Intelligence, (pp. 1850–2784).
García-Pablos, A., Cuadros, M., & Rigau, G. (2018). W2vlda: almost unsupervised system for aspect-based sentiment analysis. Expert Systems with Applications, 91, 127– 137.
Haddi, E., Liu, X., & Shi, Y. (2013). The role of text preprocessing in sentiment analysis. In Procedia Computer Science. https://doi.org/10.1016/j.procs.2013.05.005
Henríquez, C., & Guzmán, J. (2016). Las ontologías para la detección automática de aspectos en el análisis de sentimientos. Revista Prospectiva, 14(2), 90–98. https://doi.org/10.15665/rp.v14i2.750
Henríquez, C., & Guzmán, J. (2017). A Review of Sentiment Analysis in Spanish. Tecciencia, 12(22), 35–48. https://doi.org/10.18180/tecciencia.2017.22.5
Henríquez, C., Guzmán, J., & Salcedo, D. (2016). Minería de Opiniones basado en la adaptación al español de ANEW sobre opiniones acerca de hoteles Opinion. Procesamiento Del Lenguaje Natural, 41, 25–32.
Henríquez, C., Plà, F., Hurtado, L. F., & Luna, J. A. G. (2017). Análisis de sentimientos a nivel de aspecto usando ontologías y aprendizaje automático. Procesamiento Del Lenguaje Natural, 59, 49–56.
Jiménez-Zafra, S. M., Martín-Valdivia, M. T., MartínezCámara, E., & Ureña-López, L. A. (2015). Combining resources to improve unsupervised sentiment analysis at aspect-level. Journal of Information Science.
Kontopoulos, E., Berberidis, C., Dergiades, T., & Bassiliades, N. (2013). Ontology-based sentiment analysis of twitter posts. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2013.01.001
Kumar, A., Kohail, S., Kumar, A., Ekbal, A., & Biemann, C. (2016). IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis. In Proceedings of SemEval (pp. 1129–1135).
Lan, M., Xu, J., & Gao, W. (2018). Ontology similarity computation and ontology mapping using distance matrix learning approach. IAENG International Journal of Computer Science, 45, 164–176.
Lau, Raymond Y.K., Lai, Chapmann C.L., Ma, Jian, & Li, Y. (2009). Automatic domain ontology extraction for context-sensitive opinion mining (pp. 35–53).
Levy, O., Goldberg, Y., & Dagan, I. (2015). Improving distributional similarity with lessons learned from word embeddings. Transactions of the Association for Computational Linguistics, 3, 211–225.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Sentiment Analysis and Opinion Mining.
Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. (Cambridge University Press, Ed.).
Lizhen, L., Xinhui, N., & Hanshi, W. (2012). Toward a fuzzy domain sentiment ontology tree for sentiment analysis. In Image and Signal Processing (CISP), 2012 5th International Congress on. IEEE.
Manek, A. S., Shenoy, P. D., & Mohan, M. C. (2016). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 1–20.
Marcheggiani, D., Täckström, O., Esuli, A., & Sebastiani, F. (2014). Hierarchical multi-label conditional random fields for aspect-oriented opinion mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978- 3-319-06028-6_23
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2014.04.011
Padró, L., & Stanilovsky, E. (2012). FreeLing 3.0: Towards Wider Multilinguality. In LREC2012.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(12), 1–135.
Peñalver-Martinez, I., Garcia-Sanchez, F., Valencia-Garcia, R., Ángel Rodríguez-García, M., Moreno, V., Fraga, A., & Sánchez-Cervantes, J. L. (2014). Feature-based opinion mining through ontologies. Expert Systems with Applications , 41(13), 5995–6008. https://doi.org/10.1016/j.eswa.2014.03.022
Plaza-Del-Arco, F. M., Martín-Valdivia, M. T., María Jiménez-Zafra, S., Molina-González, M. D., & Martínez-Cámara, E. (2016). COPOS: Corpus Of Patient Opinions in Spanish. Application of Sentiment Analysis Techniques. Procesamiento Del Lenguaje Natural, 57, 83–90.
Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., Al-Smadi, M., … Eryiğit, G. (2016). SemEval-2016 Task 5: Aspect Based Sentiment Analysis. In Semeval (pp. 19–30).
Qiu, G., Liu, B., Bu, J., & Chen, C. (2011). Opinion Word Expansion and Target Extraction through Double Propagation. Computational Linguistics, 37, 9–27.
Recio-Garcia, J. (2006). restaurant.owl. Retrieved February 1, 2017, from http://www.disi.unige.it/person/LocoroA/download/wi lfontologies/restaurant.owl
Sidorov, S., Faizliev, A., & Balash, V. (2018). Fractality and multifractality analysis of news sentiments time series. IAENG International Journal of Applied Mathematics, 48, 90–97.
Steinberger, J., Brychcín, T., & Konkol, M. (2014). AspectLevel Sentiment Analysis in Czech. In Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 24–30).
Studer, R., Benjamins, V. R., & Fensel, D. (1998). I DATA & KNOWLEDGE ENGINEERING. Data & Knowledge Engineering, 25, 161–197.
Sun, L., Li, S., Li, J., & Lv, J. (2014). A novel context-based implicit feature extracting method. In Data Science and Advanced Analytics (DSAA), 2014 International Conference on (pp. 420–424)
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics , 37(2), 267–307.
Turney, P. D. (2002). Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics (pp. 417–424).
Vilares, D., Alonso, M. a., & Goméz-Rodríguez Carlos. (2013). A syntactic approach for opinion mining on Spanish reviews. Natural Language Engineering, 1(1), 1–26.
Wang, H., Lu, Y., & Zhai, C. (2010). Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach. ACm.
Wu, C., Wu, F., Wu, S., Yuan, Z., & Huang, Y. (2018). A hybrid unsupervised method for aspect term and opinion target extraction. Knowledge-Based Systems, 148, 66–73. https://doi.org/10.1016/J.KNOSYS.2018.01.019
Wu, Z., & Palmer, M. (1994). Verbs semantics and lexical selection. In Proceedings of the 32nd annual meeting on Association for Computational Linguistics (pp. 133– 138).
Xianghua, F., Guo, L., Yanyan, G., & Zhiqiang, W. (2013). Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems.
Zhang, W., Xu, H., & Wan, W. (2012). Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2012.02.166
Zhang, Y., & Zhu, W. (2013). Extracting Implicit Features in Online Customer Reviews for Opinion Mining. In Proceedings of the 22Nd International Conference on World Wide Web (pp. 103–104). New York, NY, USA: ACM. https://doi.org/10.1145/2487788.2487835
Zhou, L., & Chaovalit, P. (2008). Ontology-supported polarity mining. Journal of the American Society for Information Science and Technology, 98–110.
dc.rights.spa.fl_str_mv CC0 1.0 Universal
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/publicdomain/zero/1.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.spa.fl_str_mv Universidad de la Costa
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv http://www.iaeng.org/IJCS/issues_v46/issue_3/IJCS_46_3_06.pdf
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/a8448e48-5a49-414d-bb15-4f9b1c2c6beb/download
https://repositorio.cuc.edu.co/bitstreams/e7d47b43-85ff-4c67-ba02-7e38e2e03b11/download
https://repositorio.cuc.edu.co/bitstreams/6e734aaf-68ce-4d5f-91f6-76ab3a9ab05b/download
https://repositorio.cuc.edu.co/bitstreams/47f08d20-1e6c-4dec-a2dc-7bf082364c7d/download
https://repositorio.cuc.edu.co/bitstreams/9ecd91fe-8482-4f4e-89a1-553495247c23/download
https://repositorio.cuc.edu.co/bitstreams/9eec53da-1936-4634-bda9-0d858a92f0bb/download
https://repositorio.cuc.edu.co/bitstreams/0a6f704c-09b3-4912-a41f-5c1fe737cded/download
https://repositorio.cuc.edu.co/bitstreams/8f8590ec-4c32-4a5a-be2a-64374ce098da/download
https://repositorio.cuc.edu.co/bitstreams/935114ff-254e-4687-81d1-743bbdfff165/download
bitstream.checksum.fl_str_mv 42fd4ad1e89814f5e4a476b409eb708c
8a4605be74aa9ea9d79846c1fba20a33
77eafa1917bb4a4703a7f63c3c5bfd21
761502006140047b9353e38e54334b71
0d27f401540b17fdb1a51dcd97ffc180
2c1110827309d26b9169537b97543c1a
0d27f401540b17fdb1a51dcd97ffc180
2c1110827309d26b9169537b97543c1a
cf768315118f7f5e6814a5cb8cf1f6b3
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1828166837991374848
spelling Henríquez Miranda, CarlosBriceño Díaz, FreddySalcedo, Dixon2019-11-13T20:13:45Z2019-11-13T20:13:45Z2019-08-12https://hdl.handle.net/11323/5648Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This paper presents an unsupervised model for Aspect-Based Sentiment Analysis in Spanish language, which automatically extracts the aspects of opinion and determines its associated polarity. The model uses ontologies for the detection of explicit and implicit aspects, and machine learning without supervision to determine the polarity of a grammatical structure in Spanish. The unsupervised approach used, allows implementing a system quickly scalable to any language or domain. The experimental work was carried out using the dataset used in Semeval 2016 for Task 5 corresponding to Sentence-level ABSA. The implemented system obtained a 73.07 F1 value in the extraction of aspects and 84.8% accuracy in the sentiment classification. The system obtained the best results of all systems participating in the competition in the three issues mentioned above.Henríquez Miranda, CarlosBriceño Díaz, FreddySalcedo, Dixon-will be generated-orcid-0000-0002-3762-8462-600engUniversidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Aspect-basedOntologySentiment analysisUnsupervised machine learningUnsupervised model for aspect-based sentiment analysis in spanishModelo no supervisado para análisis de sentimiento basado en aspectos in españolPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionhttp://www.iaeng.org/IJCS/issues_v46/issue_3/IJCS_46_3_06.pdfAlvarez-López, T., Juncal-Martinez, J., FernándezGavilanes, M., Costa-Montenegro, E., & GonzálezCastano, F. J. (2016). Gti at semeval-2016 task 5: Svm and crf for aspect detection and unsupervised aspectbased sentiment analysis. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016) (pp. 306–311).Antònia Martí, M., Taulé, M., Teresa, M., Salud, M.-V., & Jiménez-Zafra, M. (2016). La negación en español: análisis y tipología de patrones de negación * Negation in Spanish: analysis and typology of negation patterns. Procesamiento Del Lenguaje Natural, (57), 41–48.Cadilhac, A., Benamara, F., & Aussenac-Gilles, N. (2010). Ontolexical resources for feature based opinion mining : a case-study, 77–86.Chaves, M., Larissa Freitas, & Renata Vieira. (2012). Hontology: a multilingual ontology for the accommodation sector in the tourism industry. In CTIC/STI - Comunicações a Conferências. Retrieved from http://hdl.handle.net/10884/654Church, K. W., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22–29.Cruz, F. L., Troyano, J. A., Enriquez, F., & Ortega Universidad de Sevilla AvReina Mercedes, J. (2008). Clasificación de documentos basada en la opinión: experimentos con un corpus de críticas de cine en español Experiments in sentiment classification of movie reviews in Spanish. Procesamiento de Lenguaje Natural, 41, 73–80.De Freitas, L. A., & Vieira, R. (2013). Ontology-based Feature Level Opinion Mining for Portuguese Reviews. In Proceedings of the 22nd International Conference on World Wide Web. ACM, (pp. 367–370).Dey, L., & Haque, S. M. (2008). Opinion mining from noisy text data. In Proceedings of the second workshop on Analytics for noisy unstructured text data.Dubiau, L., & Ale, J. M. (2013). Análisis de Sentimientos sobre un Corpus en Español: Experimentación con un Caso de Estudio. In Argentine Symposium on Arti_cial Intelligence, (pp. 1850–2784).García-Pablos, A., Cuadros, M., & Rigau, G. (2018). W2vlda: almost unsupervised system for aspect-based sentiment analysis. Expert Systems with Applications, 91, 127– 137.Haddi, E., Liu, X., & Shi, Y. (2013). The role of text preprocessing in sentiment analysis. In Procedia Computer Science. https://doi.org/10.1016/j.procs.2013.05.005Henríquez, C., & Guzmán, J. (2016). Las ontologías para la detección automática de aspectos en el análisis de sentimientos. Revista Prospectiva, 14(2), 90–98. https://doi.org/10.15665/rp.v14i2.750Henríquez, C., & Guzmán, J. (2017). A Review of Sentiment Analysis in Spanish. Tecciencia, 12(22), 35–48. https://doi.org/10.18180/tecciencia.2017.22.5Henríquez, C., Guzmán, J., & Salcedo, D. (2016). Minería de Opiniones basado en la adaptación al español de ANEW sobre opiniones acerca de hoteles Opinion. Procesamiento Del Lenguaje Natural, 41, 25–32.Henríquez, C., Plà, F., Hurtado, L. F., & Luna, J. A. G. (2017). Análisis de sentimientos a nivel de aspecto usando ontologías y aprendizaje automático. Procesamiento Del Lenguaje Natural, 59, 49–56.Jiménez-Zafra, S. M., Martín-Valdivia, M. T., MartínezCámara, E., & Ureña-López, L. A. (2015). Combining resources to improve unsupervised sentiment analysis at aspect-level. Journal of Information Science.Kontopoulos, E., Berberidis, C., Dergiades, T., & Bassiliades, N. (2013). Ontology-based sentiment analysis of twitter posts. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2013.01.001Kumar, A., Kohail, S., Kumar, A., Ekbal, A., & Biemann, C. (2016). IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis. In Proceedings of SemEval (pp. 1129–1135).Lan, M., Xu, J., & Gao, W. (2018). Ontology similarity computation and ontology mapping using distance matrix learning approach. IAENG International Journal of Computer Science, 45, 164–176.Lau, Raymond Y.K., Lai, Chapmann C.L., Ma, Jian, & Li, Y. (2009). Automatic domain ontology extraction for context-sensitive opinion mining (pp. 35–53).Levy, O., Goldberg, Y., & Dagan, I. (2015). Improving distributional similarity with lessons learned from word embeddings. Transactions of the Association for Computational Linguistics, 3, 211–225.Liu, B. (2012). Sentiment Analysis and Opinion Mining. Sentiment Analysis and Opinion Mining.Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. (Cambridge University Press, Ed.).Lizhen, L., Xinhui, N., & Hanshi, W. (2012). Toward a fuzzy domain sentiment ontology tree for sentiment analysis. In Image and Signal Processing (CISP), 2012 5th International Congress on. IEEE.Manek, A. S., Shenoy, P. D., & Mohan, M. C. (2016). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 1–20.Marcheggiani, D., Täckström, O., Esuli, A., & Sebastiani, F. (2014). Hierarchical multi-label conditional random fields for aspect-oriented opinion mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978- 3-319-06028-6_23Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2014.04.011Padró, L., & Stanilovsky, E. (2012). FreeLing 3.0: Towards Wider Multilinguality. In LREC2012.Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(12), 1–135.Peñalver-Martinez, I., Garcia-Sanchez, F., Valencia-Garcia, R., Ángel Rodríguez-García, M., Moreno, V., Fraga, A., & Sánchez-Cervantes, J. L. (2014). Feature-based opinion mining through ontologies. Expert Systems with Applications , 41(13), 5995–6008. https://doi.org/10.1016/j.eswa.2014.03.022Plaza-Del-Arco, F. M., Martín-Valdivia, M. T., María Jiménez-Zafra, S., Molina-González, M. D., & Martínez-Cámara, E. (2016). COPOS: Corpus Of Patient Opinions in Spanish. Application of Sentiment Analysis Techniques. Procesamiento Del Lenguaje Natural, 57, 83–90.Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., Al-Smadi, M., … Eryiğit, G. (2016). SemEval-2016 Task 5: Aspect Based Sentiment Analysis. In Semeval (pp. 19–30).Qiu, G., Liu, B., Bu, J., & Chen, C. (2011). Opinion Word Expansion and Target Extraction through Double Propagation. Computational Linguistics, 37, 9–27.Recio-Garcia, J. (2006). restaurant.owl. Retrieved February 1, 2017, from http://www.disi.unige.it/person/LocoroA/download/wi lfontologies/restaurant.owlSidorov, S., Faizliev, A., & Balash, V. (2018). Fractality and multifractality analysis of news sentiments time series. IAENG International Journal of Applied Mathematics, 48, 90–97.Steinberger, J., Brychcín, T., & Konkol, M. (2014). AspectLevel Sentiment Analysis in Czech. In Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 24–30).Studer, R., Benjamins, V. R., & Fensel, D. (1998). I DATA & KNOWLEDGE ENGINEERING. Data & Knowledge Engineering, 25, 161–197.Sun, L., Li, S., Li, J., & Lv, J. (2014). A novel context-based implicit feature extracting method. In Data Science and Advanced Analytics (DSAA), 2014 International Conference on (pp. 420–424)Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics , 37(2), 267–307.Turney, P. D. (2002). Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics (pp. 417–424).Vilares, D., Alonso, M. a., & Goméz-Rodríguez Carlos. (2013). A syntactic approach for opinion mining on Spanish reviews. Natural Language Engineering, 1(1), 1–26.Wang, H., Lu, Y., & Zhai, C. (2010). Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach. ACm.Wu, C., Wu, F., Wu, S., Yuan, Z., & Huang, Y. (2018). A hybrid unsupervised method for aspect term and opinion target extraction. Knowledge-Based Systems, 148, 66–73. https://doi.org/10.1016/J.KNOSYS.2018.01.019Wu, Z., & Palmer, M. (1994). Verbs semantics and lexical selection. In Proceedings of the 32nd annual meeting on Association for Computational Linguistics (pp. 133– 138).Xianghua, F., Guo, L., Yanyan, G., & Zhiqiang, W. (2013). Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems.Zhang, W., Xu, H., & Wan, W. (2012). Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2012.02.166Zhang, Y., & Zhu, W. (2013). Extracting Implicit Features in Online Customer Reviews for Opinion Mining. In Proceedings of the 22Nd International Conference on World Wide Web (pp. 103–104). New York, NY, USA: ACM. https://doi.org/10.1145/2487788.2487835Zhou, L., & Chaovalit, P. (2008). Ontology-supported polarity mining. Journal of the American Society for Information Science and Technology, 98–110.PublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/a8448e48-5a49-414d-bb15-4f9b1c2c6beb/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/e7d47b43-85ff-4c67-ba02-7e38e2e03b11/download8a4605be74aa9ea9d79846c1fba20a33MD53ORIGINALimage006.jpgimage006.jpgimage/jpeg16538https://repositorio.cuc.edu.co/bitstreams/6e734aaf-68ce-4d5f-91f6-76ab3a9ab05b/download77eafa1917bb4a4703a7f63c3c5bfd21MD55PrePrintUnsupervised Model.pdfPrePrintUnsupervised Model.pdfapplication/pdf674664https://repositorio.cuc.edu.co/bitstreams/47f08d20-1e6c-4dec-a2dc-7bf082364c7d/download761502006140047b9353e38e54334b71MD54THUMBNAILimage006.jpg.jpgimage006.jpg.jpgimage/jpeg13073https://repositorio.cuc.edu.co/bitstreams/9ecd91fe-8482-4f4e-89a1-553495247c23/download0d27f401540b17fdb1a51dcd97ffc180MD56PrePrintUnsupervised Model.pdf.jpgPrePrintUnsupervised Model.pdf.jpgimage/jpeg71846https://repositorio.cuc.edu.co/bitstreams/9eec53da-1936-4634-bda9-0d858a92f0bb/download2c1110827309d26b9169537b97543c1aMD57THUMBNAILimage006.jpg.jpgimage006.jpg.jpgimage/jpeg13073https://repositorio.cuc.edu.co/bitstreams/0a6f704c-09b3-4912-a41f-5c1fe737cded/download0d27f401540b17fdb1a51dcd97ffc180MD56PrePrintUnsupervised Model.pdf.jpgPrePrintUnsupervised Model.pdf.jpgimage/jpeg71846https://repositorio.cuc.edu.co/bitstreams/8f8590ec-4c32-4a5a-be2a-64374ce098da/download2c1110827309d26b9169537b97543c1aMD57TEXTPrePrintUnsupervised Model.pdf.txtPrePrintUnsupervised Model.pdf.txttext/plain51163https://repositorio.cuc.edu.co/bitstreams/935114ff-254e-4687-81d1-743bbdfff165/downloadcf768315118f7f5e6814a5cb8cf1f6b3MD5811323/5648oai:repositorio.cuc.edu.co:11323/56482024-09-17 14:16:19.179http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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