This paper describes our system used in the Aspect Based Sentiment Analysis (ABSA) task of SemEval 2016. Our system uses Maximum Entropy classifier for the aspect category detection and for the sentiment polarity task. Conditional Random Fields (CRF) are used for opinion target extraction. We achieve state-of-the-art results in 9 experiments among the constrained systems and in 2 experiments among the unconstrained systems.
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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