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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Data
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.