Abstract
Sentiment Analysis has been extensively researched in the last years. While important theoretical and practical results have been obtained, there is still room for improvement. In particular, when short sentences and low resources languages are considered. Thus, in this work we focus on sentiment analysis for Spanish Twitter messages. We explore the combination of several word representations (Word2Vec, Glove, Fastext) and Deep Neural Networks models in order to classify short texts. Previous Deep Learning approaches were unable to obtain optimal results for Spanish Twitter sentence classification. Conversely, we show promising results in that direction. Our best setting combines data augmentation, three word embeddings representations, Convolutional Neural Networks and Recurrent Neural Networks. This setup allows us to obtain state-of-the-art results on the TASS/SEPLN Spanish benchmark dataset, in terms of accuracy.
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The following tool was used to perform POS tagging: http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/.
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Ochoa-Luna, J., Ari, D. (2018). Deep Neural Network Approaches for Spanish Sentiment Analysis of Short Texts. In: Simari, G., Fermé, E., Gutiérrez Segura, F., Rodríguez Melquiades, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2018. IBERAMIA 2018. Lecture Notes in Computer Science(), vol 11238. Springer, Cham. https://doi.org/10.1007/978-3-030-03928-8_35
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