Supervised sentiment analysis in multilingual environments
Information Processing & Management, ISSN: 0306-4573, Vol: 53, Issue: 3, Page: 595-607
2017
- 74Citations
- 166Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0306457316302540; http://dx.doi.org/10.1016/j.ipm.2017.01.004; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85010664413&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0306457316302540; https://dx.doi.org/10.1016/j.ipm.2017.01.004
Elsevier BV
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