Universal, unsupervised (rule-based), uncovered sentiment analysis
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Universal, unsupervised (rule-based), uncovered sentiment analysisDate
2017-02Citation
Vilares, D., Gómez-Rodríguez, C. and Alonso, M.A. (2017) ‘Universal, unsupervised (rule-based), uncovered sentiment analysis’, Knowledge-Based Systems, 118, pp. 45–55. doi:10.1016/j.knosys.2016.11.014.
Referenced by
10.1016/j.knosys.2016.11.014
Abstract
[Abstract]: We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules. On the one hand, we exploit some of the main advantages of unsupervised algorithms: (1) the interpretability of their output, in contrast with most supervised models, which behave as a black box and (2) their robustness across different corpora and domains. On the other hand, by introducing the concept of compositional operations and exploiting syntactic information in the form of universal dependencies, we tackle one of their main drawbacks: their rigidity on data that are structured differently depending on the language concerned. Experiments show an improvement both over existing unsupervised methods, and over state-of-the-art supervised models when evaluating outside their corpus of origin. Experiments also show how the same compositional operations can be shared across languages. The system is available at http://www.grupolys.org/software/UUUSA/
Keywords
Sentiment analysis
Multilingual
Dependency parsing
Natural language processing
Multilingual
Dependency parsing
Natural language processing
Description
© 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., Gómez-Rodríguez, C. and Alonso, M.A. (2017) ‘Universal, unsupervised (rule-based), uncovered sentiment analysis’ has been accepted for publication in Knowledge-Based Systems, 118, pp. 45–55. The Version of Record is available online at https://doi.org/10.1016/j.knosys.2016.11.014.
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Rights
Atribución-NoComercial-SinDerivadas 4.0 Internacional
ISSN
0950-7051
1872-7409
1872-7409