Unsupervised Methods to Improve Aspect-Based Sentiment Analysis in Czech
Computación y Sistemas (2016)
Research topics:
Sentiment AnalysisAbstract
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.
Authors
BibTex
@article{ABSA.cicling2016, title = {Unsupervised Methods to Improve Aspect-Based Sentiment Analysis in {Czech}}, author = {Tom\'{a}\v{s} Hercig and Tom\'{a}\v{s} Brychc\'{i}n and Luk\'{a}\v{s} Svoboda and Michal Konkol and Josef Steinberger}, journal = {Computaci{\'o}n y Sistemas}, year = {2016}, volume = {20}, pages = {365-375}, number = {3}, publisher = {Centro de Investigaci{\'o}n en Computaci{\'o}n, IPN}, url = "http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/viewFile/2469/2172", doi = "10.13053/CyS-20-3-2469", issn = "ISSN 1405-5546" }
