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. 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Language identification of multilingual posts from Twitter: a case study

The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-016-0997-xThis paper describes a method for handling multi-class and multi-label classification problems based on the support vector machine formalism. This method has been applied to the language identification problem in Twitter. The system evaluation was performed mainly on a Twitter data set developed in the TweetLID workshop. This data set contains bilingual tweets written in the most commonly used Iberian languages (i.e., Spanish, Portuguese, Catalan, Basque, and Galician) as well as the English language. We address the following problems: (1) social media texts. We propose a suitable tokenization that processes the peculiarities of Twitter; (2) multilingual tweets. Since a tweet can belong to more than one language, we need to use a multi-class and multi-label classifier; (3) similar languages. We study the main confusions among similar languages; and (4) unbalanced classes. We propose threshold-based strategy to favor classes with less data. We have also studied the use of Wikipedia and the addition of new tweets in order to increase the training data set. Additionally, we have tested our system on Bergsma corpus, a collection of tweets in nine languages, focusing on confusable languages using the Cyrillic, Arabic, and Devanagari alphabets. To our knowledge, we obtained the best results published on the TweetLID data set and results that are in line with the best results published on Bergsma data set.This work has been partially funded by the project ASLP-MULAN: Audio, Speech and Language Processing for Multimedia Analytics (MINECO TIN2014-54288-C4-3-R).Pla Santamaría, F.; Hurtado Oliver, LF. (2016). Language identification of multilingual posts from Twitter: a case study. 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In: Cohen WW, Gosling S (eds) Proceedings of the fourth international conference on weblogs and social media, ICWSM 2010, Washington, DC. The AAAI Press, 23–26 May 2010Pedregosa 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 (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Pla F, Hurtado L-F (2014) Political tendency identification in twitter using sentiment analysis techniques. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers. Dublin City University and Association for Computational Linguistics, Dublin, pp 183–192Prager JM (1999) Linguini: language identification for multilingual documents. J Manage Inf Syst 16(3):71–101Ramón Quevedo J, Luaces O, Bahamonde A (2012) Multilabel classifiers with a probabilistic thresholding strategy. 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Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter

[EN] Human communication using natural language, specially in social media, is influenced by the use of figurative language like irony. Recently, several workshops are intended to explore the task of irony detection in Twitter by using computational approaches. This paper describes a model for irony detection based on the contextualization of pre-trained Twitter word embeddings by means of the Transformer architecture. This approach is based on the same powerful architecture as BERT but, differently to it, our approach allows us to use in-domain embeddings. We performed an extensive evaluation on two corpora, one for the English language and another for the Spanish language. Our system was the first ranked system in the Spanish corpus and, to our knowledge, it has achieved the second-best result on the English corpus. These results support the correctness and adequacy of our proposal. We also studied and interpreted how the multi-head self-attention mechanisms are specialized on detecting irony by means of considering the polarity and relevance of individual words and even the relationships among words. This analysis is a first step towards understanding how the multi-head self-attention mechanisms of the Transformer architecture address the irony detection problem.This work has been partially supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades and FEDER founds under project AMIC (TIN2017-85854-C4-2-R) and the GiSPRO project (PROMETEU/2018/176). Work of Jose-Angel Gonzalez is financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Hurtado Oliver, LF.; Pla Santamaría, F. (2020). Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter. Information Processing & Management. 57(4):1-15. https://doi.org/10.1016/j.ipm.2020.102262S115574Farías, D. I. H., Patti, V., & Rosso, P. (2016). Irony Detection in Twitter. ACM Transactions on Internet Technology, 16(3), 1-24. doi:10.1145/2930663Greene, R., Cushman, S., Cavanagh, C., Ramazani, J., & Rouzer, P. (Eds.). (2012). The Princeton Encyclopedia of Poetry and Poetics. doi:10.1515/9781400841424Van Hee, C., Lefever, E., & Hoste, V. (2018). We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter. Computational Linguistics, 44(4), 793-832. doi:10.1162/coli_a_00337Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735Joshi, A., Bhattacharyya, P., & Carman, M. J. (2017). Automatic Sarcasm Detection. ACM Computing Surveys, 50(5), 1-22. doi:10.1145/3124420Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019). Albert: A lite bert for self-supervised learning of language representations.Mohammad, S. M., & Turney, P. D. (2012). CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON. Computational Intelligence, 29(3), 436-465. doi:10.1111/j.1467-8640.2012.00460.xMuecke, D. C. (1978). Irony markers. Poetics, 7(4), 363-375. doi:10.1016/0304-422x(78)90011-6Potamias, R. A., Siolas, G., & Stafylopatis, A. (2019). A transformer-based approach to irony and sarcasm detection. arXiv:1911.10401.Rosso, P., Rangel, F., Farías, I. H., Cagnina, L., Zaghouani, W., & Charfi, A. (2018). A survey on author profiling, deception, and irony detection for the Arabic language. Language and Linguistics Compass, 12(4), e12275. doi:10.1111/lnc3.12275Sulis, E., Irazú Hernández Farías, D., Rosso, P., Patti, V., & Ruffo, G. (2016). Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems, 108, 132-143. doi:10.1016/j.knosys.2016.05.035Wilson, D., & Sperber, D. (1992). On verbal irony. Lingua, 87(1-2), 53-76. doi:10.1016/0024-3841(92)90025-eYus, F. (2016). Propositional attitude, affective attitude and irony comprehension. Pragmatics & Cognition, 23(1), 92-116. doi:10.1075/pc.23.1.05yusZhang, S., Zhang, X., Chan, J., & Rosso, P. (2019). Irony detection via sentiment-based transfer learning. Information Processing & Management, 56(5), 1633-1644. doi:10.1016/j.ipm.2019.04.00

Choosing the right loss function for multi-label Emotion Classification

[EN] Natural Language Processing problems has recently been benefited for the advances in Deep Learning. Many of these problems can be addressed as a multi-label classification problem. Usually, the metrics used to evaluate classification models are different from the loss functions used in the learning process. In this paper, we present a strategy to incorporate evaluation metrics in the learning process in order to increase the performance of the classifier according to the measure we are interested to favor. Concretely, we propose soft versions of the Accuracy, micro-F-1, and macro-F-1 measures that can be used as loss functions in the back-propagation algorithm. In order to experimentally validate our approach, we tested our system in an Emotion Classification task proposed at the International Workshop on Semantic Evaluation, SemEval-2018. Using a Convolutional Neural Network trained with the proposed loss functions we obtained significant improvements both for the English and the Spanish corpora.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R) and the GiSPRO project (PROMETEU/2018/176). Work of Jose-Angel Gonzalez is also financed by Universitat Politecnica de Valencia under grant PAID-01-17.Hurtado Oliver, LF.; González-Barba, JÁ.; Pla Santamaría, F. (2019). Choosing the right loss function for multi-label Emotion Classification. Journal of Intelligent & Fuzzy Systems. 36(5):4697-4708. https://doi.org/10.3233/JIFS-179019S46974708365Baccianella S. , Esuli A. and Sebastiani F. , Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining, In in Proc of LREC, 2010.Bilmes J. , Asanovic K. , Chin C.-W. and Demmel J. , Using phipac to speed error back-propagation learning, In 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 5, 1997, pp. 4153–4156.Cruz, F. L., Troyano, J. A., Pontes, B., & Ortega, F. J. (2014). Building layered, multilingual sentiment lexicons at synset and lemma levels. Expert Systems with Applications, 41(13), 5984-5994. doi:10.1016/j.eswa.2014.04.005Dembczynski K. , Jachnik A. , Kotlowski W. , Waegeman W. and Huellermeier E. , Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization, In DasguptaS. and McAllester D., editors, Proceedings of the 30th International Conference on Machine Learning volume 28 of Proceedings of Machine Learning Research, Atlanta, Georgia, USA, PMLR, 2013, pp. 1130–1138.Goodfellow I. , Bengio Y. and Courville A. , Deep Learning, MIT Press, http://www.deeplearningbook.org (2016).Hu M. and Liu B. , Mining and summarizing customer reviews, In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, New York, NY, USA, ACM, 2004, pp. 168–177.Ioffe S. and Szegedy C. , Batch normalization: Accelerating deep network training by reducing internal covariate shift, CoRR, abs/1502.03167 (2015).Janocha K. and Czarnecki W.M. , On loss functions for deep neural networks in classification, CoRR, abs/1702.05659 (2017).Krieger M. and Ahn D. , Tweetmotif: Exploratory search and topic summarization for twitter, In Proc of AAAI Conference on Weblogs and Social, 2010.Liu B. , Sentiment Analysis and Opinion Mining, A Comprehensive Introduction and Survey. Morgan & Claypool Publishers, 2012.Mikolov T. , Sutskever I. , Chen K. , Corrado G. and Dean J. , Distributed representations of words and phrases and their compositionality, CoRR, abs/1310.4546 (2013a).Mikolov T. , Chen K. , Corrado G. and Dean J. , Efficient estimation of word representations in vector space, CoRR, abs/1301.3781, 2013b.Mohammad S. , #emotional tweets, In *SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the Main Conference and the Shared Task and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), Montréal, Canada. Association for Computational Linguistics, 2012, pp. 246–255.Mohammad S. , Kiritchenko S. , Sobhani P. , Zhu X. and Cherry C. , Semeval-task 6: Detecting stance in tweets, In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016, pp. 31–41.Mohammad S.M. and Bravo-Marquez F. , WASSA-shared task on emotion intensity, CoRR, abs/1708.03700, 2017.Mohammad, S. M., & Turney, P. D. (2012). CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON. Computational Intelligence, 29(3), 436-465. doi:10.1111/j.1467-8640.2012.00460.xMohammad, S. M., Sobhani, P., & Kiritchenko, S. (2017). Stance and Sentiment in Tweets. ACM Transactions on Internet Technology, 17(3), 1-23. doi:10.1145/3003433Mohammad S.M. , Bravo-Marquez F. , Salameh M. and Kiritchenko S. , Semeval-2018 Task 1: Affect in tweets, In Proceedings of International Workshop on Semantic Evaluation (SemEval-2018), New Orleans, LA, USA, 2018.Molina-González, M. D., Martínez-Cámara, E., Martín-Valdivia, M.-T., & Perea-Ortega, J. M. (2013). Semantic orientation for polarity classification in Spanish reviews. Expert Systems with Applications, 40(18), 7250-7257. doi:10.1016/j.eswa.2013.06.076Nair V. and Hinton G.E. , Rectified linear units improve restricted boltzmann machines, In Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10, USA, 2010, pp. 807–814. Omnipress.NielsenF.Å., AFINN, 2011.Pastor-Pellicer J. , Zamora-Martínez F. , España Boquera S. and Castro Bleda M.J. , F-Measure as the Error Function to Train Neural Networks, In IWANN Proceedings, 2013.Pennebaker J. , Chung C. , Ireland M. , Gonzales A. and Booth R. , The development and psychological properties of liwc2007, 2014.Pla, F., & Hurtado, L.-F. (2016). Language identification of multilingual posts from Twitter: a case study. 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CEUR-WS.org, 2017, 2017.Wiebe J. , Wilson T. and Cardie C. , Annotating expressions of opinions and emotions in language, Language Resources and Evaluation 1(2) (2005).Wilson T. , Wiebe J. and Hoffmann P. , Recognizing contextual polarity in phrase-level sentiment analysis, In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT ’05, Stroudsburg, PA, USA, 2005, pp. 347–354. Association for Computational Linguistics.Zhang Y. and Wallace B. , A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification, In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2017, pp. 253–263. Asian Federation of Natural Language Processing

Self-attention for Twitter sentiment analysis in Spanish

[EN] This paper describes our proposal for Sentiment Analysis in Twitter for the Spanish language. The main characteristics of the system are the use of word embedding specifically trained from tweets in Spanish and the use of self-attention mechanisms that allow to consider sequences without using convolutional nor recurrent layers. These self-attention mechanisms are based on the encoders of the Transformer model. The results obtained on the Task 1 of the TASS 2019 workshop, for all the Spanish variants proposed, support the correctness and adequacy of our proposal.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R) and the GiSPRO project (PROMETEU/2018/176). Work of Jose-Angel Gonzalez is financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Hurtado Oliver, LF.; Pla Santamaría, F. (2020). Self-attention for Twitter sentiment analysis in Spanish. Journal of Intelligent & Fuzzy Systems. 39(2):2165-2175. https://doi.org/10.3233/JIFS-179881S21652175392Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.173

ELiRF-UPV at TASS 2020: TWilBERT for Sentiment Analysis and Emotion Detection in Spanish Tweets

[EN] This paper describes the participation of the ELiRF research group of the Universitat Politècnica de València in the TASS 2020 Workshop, framed within the XXXVI edition of the International Conference of the Spanish Society for the Processing of Natural Language (SEPLN). We present the approach used for the Monolingual Sentiment Analysis and Emotion Detection tasks of the workshop, as well as the results obtained. Our participation has focused mainly on employing an adaptation of BERT for text classification on the Twitter domain and the Spanish language. This system, that we have called TWilBERT, shown systematic improvements of the state of the art in almost all the tasks framed in the SEPLN conference of previous years, and also obtains the most competitive performance in the tasks addressed in this work.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R) and by the GiSPRO project (PROMETEU/2018/176). Work of José-Ángel González is financed by Universitat Politècnica de València under grant PAID-01-17.González-Barba, JÁ.; Arias-Moncho, J.; Hurtado Oliver, LF.; Pla Santamaría, F. (2020). ELiRF-UPV at TASS 2020: TWilBERT for Sentiment Analysis and Emotion Detection in Spanish Tweets. CEUR. 179-186. http://hdl.handle.net/10251/17855817918

Aspect-based sentiment analysis using ontologies and machine learning

[EN] In this paper, we present an aspect-based sentiment analysis system that allows to automatically extract the characteristics of an opinion and to determine their associated polarity. The proposed system is based on a model that uses domain ontologies for the detection of aspects and a classifier based on the Support Vector Machines formalism for assigning the polarity to the detected aspects. The experimental work was conducted using the dataset developed for Task 5, Sentence-level ABSA in SemEval 2016 for Spanish. The proposed system has obtained a 73.07 in F1 in the aspect extraction subtask (slot2) and a 46.24 of F1 in the categorization and aspect extraction subtask (slot1,2) using an ontology-based approach. For the sentiment classification subtask (slot3) an 84.79% in terms of Accuracy has been obtained using an approach based on Support Vector Machines and polarity lexicons. These results are better than those reported in SemEval.[ES] En este artículo se presenta un sistema de análisis de sentimientos a nivel de aspecto que permite extraer automáticamente las características de una opinión y determinar la polaridad asociada. El sistema propuesto está basado en un modelo que utiliza ontologías de dominio para la detección de los aspectos y un clasificador basado en Máquinas de Soporte Vectorial para la asignación de la polaridad a los aspectos detectados. El trabajo experimental se ha realizado utilizando el conjunto de datos desarrollado para la Tarea 5, Sentence-level ABSA en SemEval 2016 para el español. El sistema propuesto ha obtenido un 73.07 en F1 en la extracción de aspectos (slot2) y un 46.24 de F1 en la subtarea conjunta de categorización y extracción de aspectos (slot1,2) utilizando una aproximación basada en ontologías. Para la subtarea de clasificación de sentimientos (slot3) se ha obtenido una Accuracy de 84.79 % utilizando una aproximación basada en el uso de Máquinas de Soporte Vectorial y lexicones de polaridad. Estos valores superan los mejores resultados obtenidos en SemEval.Este trabajo ha sido parcialmente subvencionado por el proyecto ASLP-MULAN: Audio, Speech and Language Processing for Multimedia Analytics (MINECO TIN2014-54288-C4-3-R y fondos FEDER). La estancia realizada, de enero a marzo de 2017, por Carlos Henríquez en la UPV, ha sido subvencionado por el programa Colciencias (convocatoria 727), Universidad Nacional de Medellín y Universidad Autónoma del Caribe Barran-quilla.Carlos Henríquez; Pla Santamaría, F.; Hurtado Oliver, LF.; Jaime Guzmán (2017). Análisis de sentimientos a nivel de aspecto usando ontologías y aprendizaje automático. PROCESAMIENTO DEL LENGUAJE NATURAL. (59):49-56. http://hdl.handle.net/10251/101450S49565

ELIRF at MEDIAEVAL 2013: Similar Segments of Social Speech Task

This paper describes the Natural Language Engineering and Pattern Recognition group (ELiRF) approaches and results towards the Similar Segments of Social Speech Task of Me- diaEval 2013. The task involves finding segments similar to a query segment in a multimedia collection of informal, un- structured dialogs among members of a small community. Our approach has two phases. In a first phase a preprocess of the sentences is performed based on the morphology and semantics of the words. In a second phase, a searching pro- cess based on different distance measures is carried out. This has been done taking the correctly transcribed sentences and the output of an Automatic Speech Recognizer.Work funded by the Spanish Government and the E.U. under the contracts TIN2011-28169-C05 and TIN2012-38603- C02, and FPU Grant AP2010-4193García Granada, F.; Sanchís Arnal, E.; Calvo Lance, M.; Pla Santamaría, F.; Hurtado Oliver, LF. (2013). ELIRF at MEDIAEVAL 2013: Similar Segments of Social Speech Task. CEUR Workshop Proceedings. 1043:135-136. http://hdl.handle.net/10251/38151S135136104

Etiquetado léxico y análisis sintáctico superficial basado en modelos estadísticos

El objetivo general de todo sistema de Procesamiento del Lenguaje Natural (PLN) es el de obtener alguna representación del mensaje contenido de las frases. el tratamiento automático de una lengua es un problema de gran complejidad en el que intervienen diversas y complejas fuentes de conocimiento: fonética, morfología, sintaxis, semántica, pragmática, conocimiento del mundo, etc. Aunque en algunos casos estas fuentes de información se pueden considerar independientes, en general, presentan una interrelación, sin la cual, no se puede conseguir una correcta interprestación del significado y de la función de las palabras de una oración. Debido a esta complejidad, para abordar el problema de comprensión de una lengua se suele seguir una de las siguientes vias: 1) Se resuelven ciertos subproblemas más sencillos que, en algunos casos, deben aportar simplificaciones para poder ser tratados de manera automática, tales como:análisis morfológico, etiquetado léxico de textos, análisis sintáctico superficial de oraciones ligamiento preposicional, sesambiguación del sentido de las palabras, tratamiento de fenómenos lingüistícos especificos como la anáfora, elipsis, etc. 2) se simplifica el lenguaje considerando tareas restringidasm en la talla del vocabulario, la complejidad de las estructuras sintácticas utilizadas o el dominio semántico de la aplicación. Durante los últimos años podemos encontrar una gran cantidad de ejemplos que toman alguna de las vías comentadas. En reconocimiento del habla hay aplicaciones que se restringen a vocabularios acotados, sonsultas a bases de datos específicas, sistemas de diálogo sobre tareas concretas, etc. En otros campos, más directamente relacionados con el PLN, encontramos aplicaciones de traducción automática, extracción y recuperación de información, resúmenes de textos, etc, en las que, en mayor o menor medida, se restringen a dominios específicos para sonseguir resultados aceptables. Por otra parte, el echo de disponer de grandes corpus de datos, textuales u orales, anotados con información lingüística de diferente naturaleza- información morfosintáctica, análisis sintáctico total o parcial, información semantica - junto con operativos, ha proporcionado la aparición y uso de aproximaciones inductivas o métodos basados en corpus, dentro del campo de la Lingüística Computacional, que aplicamos a diferentes tareas de PLN obtienen un alto grado de prestaciones. Las aproximaciones inductivas, con o sin información estadística, resultan de gran interés para conseguir la desambiguación del Lenguaje Natural (LN) ya que, además de proporcionar resultados aceptables, utilizan modelos relativamente sencillos y sus parámetros se pueden estimar a partir de datos. Esto las hace especialmente atractivas, puesto que en el cambio de una tarea a otra, o incluso de lengua, se reduce substancialmente la intervención humana. No obstante, algunos casos de ambigüedad no pueden ser resueltos de esta forma y se debe recurrir a un experto humano para introducir, por ejemplo, ciertas reglas o restricciones que ayuden a su resolución.Pla Santamaría, F. (2000). Etiquetado léxico y análisis sintáctico superficial basado en modelos estadísticos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/5646Palanci

TWilBert: Pre-trained deep bidirectional transformers for Spanish Twitter

[EN] In recent years, the Natural Language Processing community have been moving from uncontextualized word embeddings towards contextualized word embeddings. Among these contextualized architectures, BERT stands out due to its capacity to compute bidirectional contextualized word representations. However, its competitive performance in English downstream tasks is not obtained by its multilingual version when it is applied to other languages and domains. This is especially true in the case of the Spanish language used in Twitter. In this work, we propose TWiLBERT, a specialization of BERT architecture both for the Spanish language and the Twitter domain. Furthermore, we propose a Reply Order Prediction signal to learn inter-sentence coherence in Twitter conversations, which improves the performance of TWilBERT in text classification tasks that require reasoning on sequences of tweets. We perform an extensive evaluation of TWilBERT models on 14 different text classification tasks, such as irony detection, sentiment analysis, or emotion detection. The results obtained by TWilBERT outperform the state-of-the-art systems and Multilingual BERT. In addition, we carry out a thorough analysis of the TWilBERT models to study the reasons of their competitive behavior. We release the pre-trained TWilBERT models used in this paper, along with a framework for training, evaluating, and fine-tuning TWilBERT models.This work has been partially supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades and FEDER founds under project AMIC (TIN2017-85854-C4-2-R), and the Generalitat Valenciana under GiSPRO (PROMETEU/2018/176) and GUAITA (INNVA1/2020/61) projects. Work of Jose Angel Gonzalez is financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Hurtado Oliver, LF.; Pla Santamaría, F. (2021). TWilBert: Pre-trained deep bidirectional transformers for Spanish Twitter. Neurocomputing. 426:58-69. https://doi.org/10.1016/j.neucom.2020.09.078S586942