UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (2014)
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.
Authors
BibTex
@inproceedings{S14-2145, author = "Brychc{\'i}n, Tom{\'a}{\v{s}} and Konkol, Michal and Steinberger, Josef", title = "UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis", booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)", year = "2014", publisher = "Association for Computational Linguistics", pages = "817--822", location = "Dublin, Ireland", url = "http://aclweb.org/anthology/S14-2145" }
