Resumen

HERCIG, Tomáš et al. Unsupervised Methods to Improve Aspect-Based Sentiment Analysis in Czech. Comp. y Sist. [online]. 2016, vol.20, n.3, pp.365-375. ISSN 2007-9737.  https://doi.org/10.13053/cys-20-3-2469.

We examine the effectiveness of several unsupervised methods for latent semantics discovery as features for aspect-based sentiment analysis (ABSA). We use the shared task definition from SemEval 2014. In our experiments we use labeled and unlabeled corpora within the restaurants domain for two languages: Czech and English. We show that our models improve the ABSA performance and prove that our approach is worth exploring. Moreover, we achieve new state-of-the-art results for Czech. Another important contribution of our work is that we created two new Czech corpora within the restaurant domain for the ABSA task: one labeled for supervised training, and the other (considerably larger) unlabeled for unsupervised training. The corpora are available to the research community.

Palabras llave : Aspect-based sentiment analysis; latent semantics.

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