Comparative Analyses of Multilingual Sentiment Analysis Systems for News and Social Media

Author:

Přibáň PavelORCID,Balahur AlexandraORCID

Publisher

Springer Nature Switzerland

Reference42 articles.

1. de Arruda, G.D., Roman, N.T., Monteiro, A.M.: An annotated corpus for sentiment analysis in political news. In: Proceedings of the 10th Brazilian Symposium in Information and Human Language Technology, pp. 101–110 (2015)

2. Balahur, A., Turchi, M.: Multilingual sentiment analysis using machine translation? In: Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, WASSA 2012, Stroudsburg, PA, USA, pp. 52–60. Association for Computational Linguistics (2012). dl.acm.org/citation.cfm?id=2392963.2392976

3. Balahur, A., Turchi, M.: Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Comput. Speech Lang. 28(1), 56–75 (2014)

4. Balahur, A., et al.: Resource creation and evaluation for multilingual sentiment analysis in social media texts. In: LREC, pp. 4265–4269. Citeseer (2014)

5. Barnes, J., Klinger, R., Schulte im Walde, S.: Assessing state-of-the-art sentiment models on state-of-the-art sentiment datasets. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 2–12. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/W17-5202. aclweb.org/anthology/W17-5202

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