Unsupervised Methods to Improve Aspect-Based Sentiment Analysis in Czech

Tomáš Hercig and Tomáš Brychcín and Lukáš Svoboda and Michal Konkol and Josef Steinberger
Computación y Sistemas (2016)
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Research topics

Sentiment analysis

Abstract

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.

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