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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.

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