Vilares, DavidAlonso, Miguel A.Gómez-Rodríguez, Carlos2024-04-052024-04-052013David Vilares, Miguel Ángel Alonso, and Carlos Gómez-Rodríguez. 2013. Supervised polarity classification of Spanish tweets based on linguistic knowledge. In Proceedings of the 2013 ACM symposium on Document engineering (DocEng '13). Association for Computing Machinery, New York, NY, USA, 169–172. https://doi.org/10.1145/2494266.2494300978-1-4503-1789-4http://hdl.handle.net/2183/36076This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2013 ACM symposium on Document engineering (DocEng '13). Association for Computing Machinery, New York, NY, USA, 169–172. https://doi.org/10.1145/2494266.2494300.[Abstract]: We describe a system that classifies the polarity of Spanish tweets. We adopt a hybrid approach, which combines machine learning and linguistic knowledge acquired by means of NLP. We use part-of-speech tags, syntactic dependencies and semantic knowledge as features for a supervised classifier. Lexical particularities of the language used in Twitter are taken into account in a pre-processing step. Experimental results improve over those of pure machine learning approaches and confirm the practical utility of the proposal.engTodos os dereitos reservados. All rights reserved.Document analysisLinguistic analysisMachine learningOpinion miningSentiment analysisTwitterconference outputopen access10.1145/2494266.2494300