UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis
Tomáš Brychcín
and
Michal Konkol
and
Josef Steinberger
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (2014)
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Research topics
Semantic analysis |
Sentiment analysis |
Named entitity recognition
Abstract
This paper describes our system participating
in the aspect-based sentiment analysis
task of Semeval 2014. The goal
was to identify the aspects of given target
entities and the sentiment expressed towards
each aspect. We firstly introduce
a system based on supervised machine
learning, which is strictly constrained and
uses the training data as the only source
of information. This system is then extended
by unsupervised methods for latent
semantics discovery (LDA and semantic
spaces) as well as the approach based on
sentiment vocabularies. The evaluation
was done on two domains, restaurants and
laptops. We show that our approach leads
to very promising results.