Vilares, DavidAlonso, Miguel A.Gómez-Rodríguez, Carlos2024-01-172024-01-172017-05Vilares, D., Alonso, M.A. and Gómez-Rodríguez, C. (2017) ‘Supervised sentiment analysis in multilingual environments’, Information Processing & Management, 53(3), pp. 595–607. doi:10.1016/j.ipm.2017.01.004.0306-45731873-5371http://hdl.handle.net/2183/34957© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Vilares, D., Alonso, M.A. and Gómez-Rodríguez, C. (2017) ‘Supervised sentiment analysis in multilingual environments’ has been accepted for publication in Information Processing & Management, 53(3), pp. 595–607. The Version of Record is available online at https://doi.org/10.1016/j.ipm.2017.01.004.[Abstract]: This article tackles the problem of performing multilingual polarity classification on Twitter, comparing three techniques: (1) a multilingual model trained on a multilingual dataset, obtained by fusing existing monolingual resources, that does not need any language recognition step, (2) a dual monolingual model with perfect language detection on monolingual texts and (3) a monolingual model that acts based on the decision provided by a language identification tool. The techniques were evaluated on monolingual, synthetic multilingual and code-switching corpora of English and Spanish tweets. In the latter case we introduce the first code-switching Twitter corpus with sentiment labels. The samples are labelled according to two well-known criteria used for this purpose: the SentiStrength scale and a trinary scale (positive, neutral and negative categories). The experimental results show the robustness of the multilingual approach (1) and also that it outperforms the monolingual models on some monolingual datasets.engAtribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Sentiment analysisMultilingualCode switchingjournal articleopen access10.1016/j.ipm.2017.01.004