LyS at TASS 2015: Deep Learning Experiments for Sentiment Analysis on Spanish Tweets

[Abstract]: This paper describes the participation of the LyS group at tass 2015. In this year’s edition, we used a long short-term memory neural network to address the two proposed challenges: (1) sentiment analysis at a global level and (2) aspect-based sentiment analysis on football and political tweets. The performance of this deep learning approach is compared to our last-year model, based on a square-regularized logistic regression. Experimental results show that strategies such as unsupervised pre-training, sentiment-specific word embedding or modifying the current architec- ture might be needed to achieve state-of-the-art results.[Resumen]: Este art´ıculo describe la participaci´on del grupo LyS en el tass 2015. En la edici´on de este a˜no, hemos utilizado una red neuronal denominada long short- term memory para abordar los dos retos propuestos: (1) an´alisis del sentimiento a nivel global y (2) an´alisis del sentimiento a nivel de aspectos sobre tuits futbol´ısticos y de pol´ıtica. El rendimiento obtenido por esta red de aprendizaje profundo es comparado con el de nuestro sistema del a˜no pasado, una regresi´on log´ıstica con una regularizaci´on cuadr´atica. Los resultados experimentales muestran que es necesario incluir estrategias como pre-entrenamiento no supervisado, t´ecnicas espec´ıficas para representar palabras como vectores o modificar la arquitectura actual para alcanzar resultados acordes con el estado del arte.This research is supported by the Ministerio de Economía y Competitividad y FEDER (FFI2014-51978-C2) and Xunta de Galicia (R2014/034). The first author is funded by the Ministerio de Educación, Cultura y Deporte (FPU13/01180).Xunta de Galicia; R2014/03

On the Processing and Analysis of Microtexts: From Normalization to Semantics

Trátase dun resumo estendido da ponencia[Abstract] User-generated content published on microblogging social platforms constitutes an invaluable source of information for diverse purposes: health surveillance, business intelligence, political analysis, etc. We present an overview of our work on the field of microtext processing covering the entire pipeline: from input preprocessing to high-level text mining applications.Ministerio de Economía Industria y Competitividad; FFI2014-51978-C2-2-RMinisterio de Economía Industria y Competitividad; TIN2017–85160–C2–1-RMinisterio de Economía Industria y Competitividad; TIN2017–85160–C2–2-RMinisterio de Economía Industria y Competitividad; BES-2015-073768Ministerio de Economía Industria y Competitividad; FFI2014-51978-C2-1-RXunta de Galicia; ED431D 2017/1

TASS 2015 – La evolución de los sistemas de análisis de opiniones para español

El análisis de opiniones en microblogging sigue siendo una tarea de actualidad, que permite conocer la orientación de las opiniones que minuto tras minuto se publican en medios sociales en Internet. TASS es un taller de participación que tiene como finalidad promover la investigación y desarrollo de nuevos algoritmos, recursos y técnicas aplicado al análisis de opiniones en español. En este artículo se describe la cuarta edición de TASS, resumiendo las principales aportaciones de los sistemas presentados, analizando los resultados y mostrando la evolución de los mismos. Además de analizar brevemente los sistemas que se presentaron, se presenta un nuevo corpus de tweets etiquetados en el dominio político, que se desarrolló para la tarea de Análisis de Opiniones a nivel de Aspecto.Sentiment Analysis in microblogging continues to be a trendy task, which allows to understand the polarity of the opinions published in social media. TASS is a workshop whose goal is to boost the research on Sentiment Analysis in Spanish. In this paper we describe the fourth edition of TASS, showing a summary of the systems, analyzing the results to check their evolution. In addition to a brief description of the participant systems, a new corpus of tweets is presented, compiled for the Sentiment Analysis at Aspect Level task.This work has been partially supported by a grant from the Fondo Europeo de Desarrollo Regional (FEDER), REDES project (TIN2015-65136-C2-1-R) and Ciudad2020 (INNPRONTA IPT-20111006) from the Spanish Government

Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization

[ES] Hoy en día, la sociedad tiene acceso y posibilidad de contribuir a grandes cantidades de contenidos presentes en Internet, como redes sociales, periódicos online, foros, blogs o plataformas de contenido multimedia. Todo este tipo de medios han tenido, durante los últimos años, un impacto abrumador en el día a día de individuos y organizaciones, siendo actualmente medios predominantes para compartir, debatir y analizar contenidos online. Por este motivo, resulta de interés trabajar sobre este tipo de plataformas, desde diferentes puntos de vista, bajo el paraguas del Procesamiento del Lenguaje Natural. En esta tesis nos centramos en dos áreas amplias dentro de este campo, aplicadas al análisis de contenido en línea: análisis de texto en redes sociales y resumen automático. En paralelo, las redes neuronales también son un tema central de esta tesis, donde toda la experimentación se ha realizado utilizando enfoques de aprendizaje profundo, principalmente basados en mecanismos de atención. Además, trabajamos mayoritariamente con el idioma español, por ser un idioma poco explorado y de gran interés para los proyectos de investigación en los que participamos. Por un lado, para el análisis de texto en redes sociales, nos enfocamos en tareas de análisis afectivo, incluyendo análisis de sentimientos y detección de emociones, junto con el análisis de la ironía. En este sentido, se presenta un enfoque basado en Transformer Encoders, que consiste en contextualizar \textit{word embeddings} pre-entrenados con tweets en español, para abordar tareas de análisis de sentimiento y detección de ironía. También proponemos el uso de métricas de evaluación como funciones de pérdida, con el fin de entrenar redes neuronales, para reducir el impacto del desequilibrio de clases en tareas \textit{multi-class} y \textit{multi-label} de detección de emociones. Adicionalmente, se presenta una especialización de BERT tanto para el idioma español como para el dominio de Twitter, que tiene en cuenta la coherencia entre tweets en conversaciones de Twitter. El desempeño de todos estos enfoques ha sido probado con diferentes corpus, a partir de varios \textit{benchmarks} de referencia, mostrando resultados muy competitivos en todas las tareas abordadas. Por otro lado, nos centramos en el resumen extractivo de artículos periodísticos y de programas televisivos de debate. Con respecto al resumen de artículos, se presenta un marco teórico para el resumen extractivo, basado en redes jerárquicas siamesas con mecanismos de atención. También presentamos dos instancias de este marco: \textit{Siamese Hierarchical Attention Networks} y \textit{Siamese Hierarchical Transformer Encoders}. Estos sistemas han sido evaluados en los corpora CNN/DailyMail y NewsRoom, obteniendo resultados competitivos en comparación con otros enfoques extractivos coetáneos. Con respecto a los programas de debate, se ha propuesto una tarea que consiste en resumir las intervenciones transcritas de los ponentes, sobre un tema determinado, en el programa "La Noche en 24 Horas". Además, se propone un corpus de artículos periodísticos, recogidos de varios periódicos españoles en línea, con el fin de estudiar la transferibilidad de los enfoques propuestos, entre artículos e intervenciones de los participantes en los debates. Este enfoque muestra mejores resultados que otras técnicas extractivas, junto con una transferibilidad de dominio muy prometedora.[CA] Avui en dia, la societat té accés i possibilitat de contribuir a grans quantitats de continguts presents a Internet, com xarxes socials, diaris online, fòrums, blocs o plataformes de contingut multimèdia. Tot aquest tipus de mitjans han tingut, durant els darrers anys, un impacte aclaparador en el dia a dia d'individus i organitzacions, sent actualment mitjans predominants per compartir, debatre i analitzar continguts en línia. Per aquest motiu, resulta d'interès treballar sobre aquest tipus de plataformes, des de diferents punts de vista, sota el paraigua de l'Processament de el Llenguatge Natural. En aquesta tesi ens centrem en dues àrees àmplies dins d'aquest camp, aplicades a l'anàlisi de contingut en línia: anàlisi de text en xarxes socials i resum automàtic. En paral·lel, les xarxes neuronals també són un tema central d'aquesta tesi, on tota l'experimentació s'ha realitzat utilitzant enfocaments d'aprenentatge profund, principalment basats en mecanismes d'atenció. A més, treballem majoritàriament amb l'idioma espanyol, per ser un idioma poc explorat i de gran interès per als projectes de recerca en els que participem. D'una banda, per a l'anàlisi de text en xarxes socials, ens enfoquem en tasques d'anàlisi afectiu, incloent anàlisi de sentiments i detecció d'emocions, juntament amb l'anàlisi de la ironia. En aquest sentit, es presenta una aproximació basada en Transformer Encoders, que consisteix en contextualitzar \textit{word embeddings} pre-entrenats amb tweets en espanyol, per abordar tasques d'anàlisi de sentiment i detecció d'ironia. També proposem l'ús de mètriques d'avaluació com a funcions de pèrdua, per tal d'entrenar xarxes neuronals, per reduir l'impacte de l'desequilibri de classes en tasques \textit{multi-class} i \textit{multi-label} de detecció d'emocions. Addicionalment, es presenta una especialització de BERT tant per l'idioma espanyol com per al domini de Twitter, que té en compte la coherència entre tweets en converses de Twitter. El comportament de tots aquests enfocaments s'ha provat amb diferents corpus, a partir de diversos \textit{benchmarks} de referència, mostrant resultats molt competitius en totes les tasques abordades. D'altra banda, ens centrem en el resum extractiu d'articles periodístics i de programes televisius de debat. Pel que fa a l'resum d'articles, es presenta un marc teòric per al resum extractiu, basat en xarxes jeràrquiques siameses amb mecanismes d'atenció. També presentem dues instàncies d'aquest marc: \textit{Siamese Hierarchical Attention Networks} i \textit{Siamese Hierarchical Transformer Encoders}. Aquests sistemes s'han avaluat en els corpora CNN/DailyMail i Newsroom, obtenint resultats competitius en comparació amb altres enfocaments extractius coetanis. Pel que fa als programes de debat, s'ha proposat una tasca que consisteix a resumir les intervencions transcrites dels ponents, sobre un tema determinat, al programa "La Noche en 24 Horas". A més, es proposa un corpus d'articles periodístics, recollits de diversos diaris espanyols en línia, per tal d'estudiar la transferibilitat dels enfocaments proposats, entre articles i intervencions dels participants en els debats. Aquesta aproximació mostra millors resultats que altres tècniques extractives, juntament amb una transferibilitat de domini molt prometedora.[EN] Nowadays, society has access, and the possibility to contribute, to large amounts of the content present on the internet, such as social networks, online newspapers, forums, blogs, or multimedia content platforms. These platforms have had, during the last years, an overwhelming impact on the daily life of individuals and organizations, becoming the predominant ways for sharing, discussing, and analyzing online content. Therefore, it is very interesting to work with these platforms, from different points of view, under the umbrella of Natural Language Processing. In this thesis, we focus on two broad areas inside this field, applied to analyze online content: text analytics in social media and automatic summarization. Neural networks are also a central topic in this thesis, where all the experimentation has been performed by using deep learning approaches, mainly based on attention mechanisms. Besides, we mostly work with the Spanish language, due to it is an interesting and underexplored language with a great interest in the research projects we participated in. On the one hand, for text analytics in social media, we focused on affective analysis tasks, including sentiment analysis and emotion detection, along with the analysis of the irony. In this regard, an approach based on Transformer Encoders, based on contextualizing pretrained Spanish word embeddings from Twitter, to address sentiment analysis and irony detection tasks, is presented. We also propose the use of evaluation metrics as loss functions, in order to train neural networks for reducing the impact of the class imbalance in multi-class and multi-label emotion detection tasks. Additionally, a specialization of BERT both for the Spanish language and the Twitter domain, that takes into account inter-sentence coherence in Twitter conversation flows, is presented. The performance of all these approaches has been tested with different corpora, from several reference evaluation benchmarks, showing very competitive results in all the tasks addressed. On the other hand, we focused on extractive summarization of news articles and TV talk shows. Regarding the summarization of news articles, a theoretical framework for extractive summarization, based on siamese hierarchical networks with attention mechanisms, is presented. Also, we present two instantiations of this framework: Siamese Hierarchical Attention Networks and Siamese Hierarchical Transformer Encoders. These systems were evaluated on the CNN/DailyMail and the NewsRoom corpora, obtaining competitive results in comparison to other contemporary extractive approaches. Concerning the TV talk shows, we proposed a text summarization task, for summarizing the transcribed interventions of the speakers, about a given topic, in the Spanish TV talk shows of the ``La Noche en 24 Horas" program. In addition, a corpus of news articles, collected from several Spanish online newspapers, is proposed, in order to study the domain transferability of siamese hierarchical approaches, between news articles and interventions of debate participants. This approach shows better results than other extractive techniques, along with a very promising domain transferability.González Barba, JÁ. (2021). Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172245TESI

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. M. & Martínez-Barco, P. (2015). Evaluating a sentiment analysis approach from a business point of view. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 93–98), Alicante, Spain, September 15, 2015.Fernández, J., Gutiérrez, Y., Gómez, J.M., Martínez-Barco, P., Montoyo A., & Muñoz, R. (2013). Sentiment analysis of Spanish Tweets using a ranking algorithm and skipgrams. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Frank, E., Hall, M. A., & Witten, I. H. (2016). The WEKA workbench. Online appendix for “Data mining: Practical machine learning tools and techniques” (4th ed.). Burlington: Morgan Kaufmann.Gamallo, P., García, M. & Fernández-Lanza, S. (2013). TASS: A Naive-Bayes strategy for sentiment analysis on Spanish tweets. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.García Cumbreras, M. Á., Martínez Cámara, E., Villena-Román, J., & García Morera, J. (2016a). TASS 2015—The evolution of the Spanish opinion mining systems. Procesamiento del Lenguaje Natural.García Cumbreras, M. Á., Villena Román, J., Martínez Cámara, E., Díaz Galiano, M. C., Martín Valdivia, M. T., & Ureña López, L. A. (2016b). Overview of TASS 2016. In Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 13–21), Salamanca, Spain, September 13th, 2016.García, D., & Thelwall, M. (2013). Political alignment and emotional expression in Spanish Tweets. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Hagen, M., Potthast, M., Büchner, M., & Stein, B. (2015). Webis: An ensemble for twitter sentiment detection. 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ELiRF-UPV en TASS 2016: Análisis de Sentimientos en Twitter. In Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 47–51), Salamanca, Spain, September 13th, 2016.Hurtado, L. F., Pla, F., & Buscaldi, D. (2015). ELiRF-UPV en TASS 2015: Análisis de Sentimientos en Twitter. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 75–79), Alicante, Spain, September 15, 2015.Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169–2188.Jiménez Zafra, S. M., Martínez Cámara, E., Martín Valdivia, M. T., & Ureña López, L. A. (2014) SINAI-ESMA: An unsupervised approach for sentiment analysis in Twitter. In Proceedings of the TASS workshop at SEPLN 2014.Liu, B. (2012). Sentiment analysis and opinion mining. A comprehensive introduction and survey. San Rafael: Morgan & Claypool Publishers.Liu, B., Hu, M., & Cheng, J. (2005). Opinion observer: Analyzing and comparing opinions on the web. In Proceedings of the 14th international conference on world wide web (pp. 342–351), ACM, New York, NY, USA, WWW ’05, doi: 10.1145/1060745.1060797 , http://doi.acm.org/10.1145/1060745.1060797Martínez-Cámara, E., Martín-Valdivia, M. T., Ureña-López, L. A., & Montejo-Raéz, A. (2014). Sentiment analysis in Twitter. Natural Language Engineering, 1(1), 1–28.Martínez-Cámara, E., García-Cumbreras, M.Á., Martín-Valdivia, M. T., & López, L. A. U. (2015). SINAI-EMMA: Vectores de Palabras para el Análisis de Opiniones en Twitter. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 41–46), Alicante, Spain, September 15, 2015.Martín-Wanton, T., & de Albornoz, J. C. (2012). UNED at TASS 2012: Polarity classification and trending topic system. Technical report, http://www.sepln.org/workshops/tass/2012/participation.php .Martínez-Cámara, E., Ángel García-Cumbreras, M., Martín-Valdivia, M. T., & Ureña-López, L. A. (2013). SINAI-EMML: Combinación de Recursos Lingüíticos para el Análisis de la Opinión en Twitter. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Martínez-Cámara, E., Martín-Valdivia, M. T., Molina-González, M. D., & Ureña-López, L. A. (2013). Bilingual experiments on an opinion comparable corpus. In Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 87–93).Mendizabal, I., & Carandell, J. (2015). BittenPotato: Tweet sentiment analysis by combining multiple classifiers. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 71–74), Alicante, Spain, September 15, 2015.Mohammad, S., Kiritchenko, S., & Zhu, X. (2013). 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