Improving Spanish Polarity Classification Combining Different Linguistic Resources

Sentiment analysis is a challenging task which is attracting the attention of researchers. However, most of work is only focused on English documents, perhaps due to the lack of linguistic resources for other languages. In this paper, we present several Spanish opinion mining resources in order to develop a polarity classification system. In addition, we propose the combination of different features extracted from each resource in order to train a classifier over two different opinion corpora. We prove that the integration of knowledge from several resources can improve the final Spanish polarity classification system. The good results encourage us to continue developing sentiment resources for Spanish, and studying the combination of features extracted from different resourcesMinisterio de Economía y Competitividad TIN2012-38536-C03-0Junta de Andalucía P11-TIC-7684Universidad de Jaén CEATIC-2013-0

A Knowledge-Based Model for Polarity Shifters

[EN] Polarity shifting can be considered one of the most challenging problems in the context of Sentiment Analysis. Polarity shifters, also known as contextual valence shifters (Polanyi and Zaenen 2004), are treated as linguistic contextual items that can increase, reduce or neutralise the prior polarity of a word called focus included in an opinion. The automatic detection of such items enhances the performance and accuracy of computational systems for opinion mining, but this challenge remains open, mainly for languages other than English. From a symbolic approach, we aim to advance in the automatic processing of the polarity shifters that affect the opinions expressed on tweets, both in English and Spanish. To this end, we describe a novel knowledge-based model to deal with three dimensions of contextual shifters: negation, quantification, and modality (or irrealis).This work is part of the project grant PID2020-112827GB-I00, funded by MCIN/AEI/10.13039/501100011033, and the SMARTLAGOON project [101017861], funded by Horizon 2020 - European Union Framework Programme for Research and Innovation.Blázquez-López, Y. (2022). A Knowledge-Based Model for Polarity Shifters. Journal of Computer-Assisted Linguistic Research. 6:87-107. https://doi.org/10.4995/jclr.2022.1880787107

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Spanish sentiment analysis in Twitter at the TASS workshop

[EN] This paper describes a support vector machine-based approach to different tasks related to sentiment analysis in Twitter for Spanish. We focus on parameter optimization of the models and the combination of several models by means of voting techniques. We evaluate the proposed approach in all the tasks that were defined in the five editions of the TASS workshop, between 2012 and 2016. TASS has become a framework for sentiment analysis tasks that are focused on the Spanish language. We describe our participation in this competition and the results achieved, and then we provide an analysis of and comparison with the best approaches of the teams who participated in all the tasks defined in the TASS workshops. To our knowledge, our results exceed those published to date in the sentiment analysis tasks of the TASS workshops.This work has been partially funded by the Spanish MINECO and FEDER founds under project ASLP-MULAN: Audio, Speech and Language Processing for Multimedia Analytics, TIN2014-54288-C4-3-R.Pla Santamaría, F.; Hurtado Oliver, LF. (2018). Spanish sentiment analysis in Twitter at the TASS workshop. Language Resources and Evaluation. 52(2):645-672. https://doi.org/10.1007/s10579-017-9394-7S645672522Álvarez-López, T., Juncal-Martínez, J., Fernández-Gavilanes, M., Costa-Montenegro, E., González-Castaño, F.J., Cerezo-Costas, H. , & Celix-Salgado, D. (2015). GTI-gradiant at TASS 2015: A hybrid approach for sentiment analysis in Twitter. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 35–40), Alicante, Spain, September 15, 2015.Álvarez-López, T., Fernández-Gavilanes, M., García-Méndez, S., Juncal-Martínez, J., & González-Castaño, F.J. (2016). GTI at TASS 2016: Supervised approach for aspect based sentiment analysis in Twitter. In Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 53–57), Salamanca, Spain, September 13th, 2016.Araque, O., Corcuera, I., Román, C., Iglesias, C. A., & Sánchez-Rada, J. F. (2015). Aspect based sentiment analysis of Spanish tweets. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 29–34), Alicante, Spain, September 15, 2015.Balahur, A., & Perea-Ortega, J. M. (2013). Experiments using varying sizes and machine translated data for sentiment analysis in Twitter. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Barbosa, L., & Feng, J. (2010). Robust sentiment detection on Twitter from biased and noisy data. In Proceedings of the 23rd international conference on computational linguistics: posters, association for computational linguistics (pp. 36–44).Batista, F., & Ribeiro, R. (2012). The L2F Strategy for Sentiment Analysis and Topic Classification. Technical report, http://www.sepln.org/workshops/tass/2012/participation.php .Casasola Murillo, E., & Marín Raventós, G. (2016). Evaluación de Modelos de Representación del Texto con Vectores de Dimensiónn Reducida para Análisis de Sentimiento. In Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 23–28), Salamanca, Spain, September 13th, 2016.Castellano, A., Cigarrán, J. & García-Serrano, A. (2012). UNED @ TASS: Using IR techniques for topic-based sentiment analysis through divergence models. Technical report, http://www.sepln.org/workshops/tass/2012/participation.php .Castellanos-González, A., Cigarrán-Recuero, J. & García-Serrano, A. (2013). UNED LSI @ TASS 2013: Considerations about textual representation for IR based tweet classification. In: Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Cerón-Guzmán, J. A. (2016). JACERONG at TASS 2016: An ensemble classifier for sentiment analysis of Spanish tweets at global level. In: Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 35–39), Salamanca, Spain, September 13th, 2016.del-Hoyo-Alonso, R., Hupont, I., & Lacueva, F. (2013). Affective polarity word discovering by means of artificial general intelligence techniques. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.del-Hoyo-Alonso, R., de la Vega Rodrigalvarez-Chamorro, M., Vea-Murguía, J., & Montañes-Salas, R. M. (2015). Ensemble algorithm with syntactical tree features to improve the opinion analysis. In Proceedings of TASS 2015: workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 53–58), Alicante, Spain, September 15, 2015.Deriu, J., Gonzenbach, M., Uzdilli, F., Lucchi, A., De Luca, V., & Jaggi, M. (2016). Swisscheese at semeval-2016 task 4: Sentiment classification using an ensemble of convolutional neural networks with distant supervision. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016) (pp. 1124–1128), Association for Computational Linguistics, San Diego, California, http://www.aclweb.org/anthology/S16-1173 .Díaz-Galiano, M. C., & Montejo-Ráez, A. (2015). Participación de SINAI DW2Vec en TASS 2015. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 59–64), Alicante, Spain, September 15, 2015.Fernández, J., Gutiérrez, Y., Tomás, D., Gómez, J. 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