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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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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Resumen: El presente paper presenta un conjunto de experimentos para abordar la tarea de clasificación global de polaridad de tweets en español del TASS 2015. En este trabajo se hace una comparación entre los principales algoritmos de clasificación supervisados para el Análisis de Sentimientos: Support Vector Machines, Naive Bayes, Entropía Máxima y Árboles de Decisión. Se propone también mejorar el rendimiento de estos clasificadores utilizando una técnica de reducción de clases y luego un algoritmo de votación llamado Naive Voting. Los resultados muestran que nuestra propuesta supera los otros métodos de aprendizaje de máquina propuestos en este trabajo. Palabras clave: Análisis de Sentimientos, Métodos Supervisados, Tweets Españoles Abstract: This paper presents a set of experiments to address the global polarity classification task of Spanish Tweets of TASS 2015. In this work, we compare the main supervised classification algorithms for Sentiment Analysis: Support Vector Machines, Naive Bayes, Maximum Entropy and Decision Trees. We propose to improve the performance of these classifiers using a class reduction technique and then a voting algorithm called Naive Voting. Results show that our proposal outperforms the other machine learning methods proposed in this work

A two-stage deep learning approach for extracting entities and relationships from medical texts

This Work Presents A Two-Stage Deep Learning System For Named Entity Recognition (Ner) And Relation Extraction (Re) From Medical Texts. These Tasks Are A Crucial Step To Many Natural Language Understanding Applications In The Biomedical Domain. Automatic Medical Coding Of Electronic Medical Records, Automated Summarizing Of Patient Records, Automatic Cohort Identification For Clinical Studies, Text Simplification Of Health Documents For Patients, Early Detection Of Adverse Drug Reactions Or Automatic Identification Of Risk Factors Are Only A Few Examples Of The Many Possible Opportunities That The Text Analysis Can Offer In The Clinical Domain. In This Work, Our Efforts Are Primarily Directed Towards The Improvement Of The Pharmacovigilance Process By The Automatic Detection Of Drug-Drug Interactions (Ddi) From Texts. Moreover, We Deal With The Semantic Analysis Of Texts Containing Health Information For Patients. Our Two-Stage Approach Is Based On Deep Learning Architectures. Concretely, Ner Is Performed Combining A Bidirectional Long Short-Term Memory (Bi-Lstm) And A Conditional Random Field (Crf), While Re Applies A Convolutional Neural Network (Cnn). Since Our Approach Uses Very Few Language Resources, Only The Pre-Trained Word Embeddings, And Does Not Exploit Any Domain Resources (Such As Dictionaries Or Ontologies), This Can Be Easily Expandable To Support Other Languages And Clinical Applications That Require The Exploitation Of Semantic Information (Concepts And Relationships) From Texts...This work was supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R)

Introducción a la tarea compartida Tweet-Norm 2013: Normalización léxica de tuits en español

En este artículo se presenta una introducción a la tarea Tweet-Norm 2013 : descripción, corpora, anotación, preproceso, sistemas presentados y resultados obtenidos.Postprint (published version

Comparative analysis of the computational characteristics in modern sentiment analysis systems for Spanish

Existen múltiples sistemas para análisis de sentimiento con diseños heterogéneos y niveles variados de desempeño. En este artículo se presenta un modelo de generación de especificaciones computacionales de los sistemas para identificación de polaridad tendientes a facilitar comparaciones más profundas y detalladas de las técnicas que se utilizan. Buscamos crear conciencia entre los investigadores de la información necesaria para la construcción de especificaciones que permitan replicar sistemas. A su vez, discutimos las dificultades que se tiene al evaluar y al hacer comparaciones congruentes entre sistemas e ir más allá del resultado que se puede obtener para una tarea específica sobre un conjunto de datos particular. Estamos convencidos de que una estructuración completa y clara de todos los procesos y de los ajustes a que son sometidos los trabajos presentados en las competencias es crucial para enriquecer el conocimiento del uso de estrategias hacia la mejora general de los sistemas.There are multiple systems for sentiment analysis with heterogeneous designs and varying levels of performance. In this paper, we propose a model of computational specifications of polarity identification systems. The model makes easier the comparison of the different techniques used. We seek to create awareness among researchers of the necessary information for the elaboration of specifications that allow the replication of systems. Additionally, we discuss the difficulties of evaluating and conducting consistent comparisons among systems, and going beyond the result that can be obtained for a specific task on a particular data set. We are convinced that a complete and clear framework that encompasses all the modules of the systems that participate in competitions is crucial to enrich the knowledge for improving the state-of-the-art in sentiment analysis.Se agradece el apoyo de MICITT y CONICIT del Gobierno de Costa Rica y al proyecto IN403016 DGAPA-PAPIIT de la UNAM. Eugenio Martínez Cámara fue financiado por el programa Juan de la Cierva Formación (FJCI-2016-28353) del Gobierno de España