Show simple item record

dc.contributor.authorHenríquez Miranda, Carlos
dc.contributor.authorBriceño Díaz, Freddy
dc.contributor.authorSalcedo, Dixon
dc.description.abstractThis 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
dc.publisherUniversidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.subjectSentiment analysisspa
dc.subjectUnsupervised machine learningspa
dc.titleUnsupervised model for aspect-based sentiment analysis in spanishspa
dc.title.alternativeModelo no supervisado para análisis de sentimiento basado en aspectos in españolspa
dcterms.referencesAlvarez-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).spa
dcterms.referencesAntònia 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–
dcterms.referencesCadilhac, A., Benamara, F., & Aussenac-Gilles, N. (2010). Ontolexical resources for feature based opinion mining : a case-study, 77–
dcterms.referencesChaves, 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
dcterms.referencesChurch, K. W., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22–29.
dcterms.referencesCruz, 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–
dcterms.referencesDe 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).
dcterms.referencesDey, L., & Haque, S. M. (2008). Opinion mining from noisy text data. In Proceedings of the second workshop on Analytics for noisy unstructured text
dcterms.referencesDubiau, 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).
dcterms.referencesGarcía-Pablos, A., Cuadros, M., & Rigau, G. (2018). W2vlda: almost unsupervised system for aspect-based sentiment analysis. Expert Systems with Applications, 91, 127– 137.
dcterms.referencesHaddi, E., Liu, X., & Shi, Y. (2013). The role of text preprocessing in sentiment analysis. In Procedia Computer Science.
dcterms.referencesHenríquez, C., & Guzmán, J. (2016). Las ontologías para la detección automática de aspectos en el análisis de sentimientos. Revista Prospectiva, 14(2), 90–98.
dcterms.referencesHenríquez, C., & Guzmán, J. (2017). A Review of Sentiment Analysis in Spanish. Tecciencia, 12(22), 35–48.
dcterms.referencesHenríquez, 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.
dcterms.referencesHenríquez, 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.
dcterms.referencesJiménez-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.
dcterms.referencesKontopoulos, E., Berberidis, C., Dergiades, T., & Bassiliades, N. (2013). Ontology-based sentiment analysis of twitter posts. Expert Systems with Applications.
dcterms.referencesKumar, 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).
dcterms.referencesLan, 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.
dcterms.referencesLau, Raymond Y.K., Lai, Chapmann C.L., Ma, Jian, & Li, Y. (2009). Automatic domain ontology extraction for context-sensitive opinion mining (pp. 35–53).
dcterms.referencesLevy, 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.
dcterms.referencesLiu, B. (2012). Sentiment Analysis and Opinion Mining. Sentiment Analysis and Opinion Mining.
dcterms.referencesLiu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. (Cambridge University Press, Ed.).
dcterms.referencesLizhen, 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.
dcterms.referencesManek, 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–
dcterms.referencesMarcheggiani, 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). 3-319-06028-6_23
dcterms.referencesMedhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal.
dcterms.referencesPadró, L., & Stanilovsky, E. (2012). FreeLing 3.0: Towards Wider Multilinguality. In LREC2012.
dcterms.referencesPang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(12), 1–135.
dcterms.referencesPeñalver-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.
dcterms.referencesPlaza-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.
dcterms.referencesPontiki, 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).
dcterms.referencesQiu, G., Liu, B., Bu, J., & Chen, C. (2011). Opinion Word Expansion and Target Extraction through Double Propagation. Computational Linguistics, 37, 9–27.
dcterms.referencesRecio-Garcia, J. (2006). restaurant.owl. Retrieved February 1, 2017, from lfontologies/restaurant.owlspa
dcterms.referencesSidorov, S., Faizliev, A., & Balash, V. (2018). Fractality and multifractality analysis of news sentiments time series. IAENG International Journal of Applied Mathematics, 48, 90–97.
dcterms.referencesSteinberger, 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).
dcterms.referencesStuder, R., Benjamins, V. R., & Fensel, D. (1998). I DATA & KNOWLEDGE ENGINEERING. Data & Knowledge Engineering, 25, 161–197.
dcterms.referencesSun, 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)
dcterms.referencesTaboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics , 37(2), 267–307.
dcterms.referencesTurney, 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).
dcterms.referencesVilares, D., Alonso, M. a., & Goméz-Rodríguez Carlos. (2013). A syntactic approach for opinion mining on Spanish reviews. Natural Language Engineering, 1(1), 1–26.
dcterms.referencesWang, H., Lu, Y., & Zhai, C. (2010). Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach. ACm.
dcterms.referencesWu, 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.
dcterms.referencesWu, Z., & Palmer, M. (1994). Verbs semantics and lexical selection. In Proceedings of the 32nd annual meeting on Association for Computational Linguistics (pp. 133– 138).
dcterms.referencesXianghua, 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.
dcterms.referencesZhang, W., Xu, H., & Wan, W. (2012). Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Systems with Applications.
dcterms.referencesZhang, 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.
dcterms.referencesZhou, L., & Chaovalit, P. (2008). Ontology-supported polarity mining. Journal of the American Society for Information Science and Technology, 98–

Files in this item


This item appears in the following Collection(s)

  • Artículos científicos [2636]
    Artículos de investigación publicados por miembros de la comunidad universitaria.

Show simple item record

CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal