Unsupervised Methods to Improve Aspect-Based Sentiment Analysis in Czech
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| Format: | Artículo científico |
| Sprache: | en |
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Instituto Politécnico Nacional
2016
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| _version_ | 1876441586762514432 |
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| author | Tomás Hercig |
| author_facet | Tomás Hercig |
| contents | Unsupervised Methods to Improve Aspect-Based Sentiment Analysis in Czech Tomás Hercig Tomás Brychcín Lukás Svoboda Michal Konkol Josef Steinberger Computación Aspect latent semantics based sentiment analysis 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. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61547469006 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.20 |
| format | Artículo científico |
| id | redalyc_61547469006 |
| institution | Redalyc |
| language | en |
| publishDate | 2016 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Unsupervised Methods to Improve Aspect-Based Sentiment Analysis in Czech Tomás Hercig Computación Aspect latent semantics based sentiment analysis Unsupervised Methods to Improve Aspect-Based Sentiment Analysis in Czech Tomás Hercig Tomás Brychcín Lukás Svoboda Michal Konkol Josef Steinberger Computación Aspect latent semantics based sentiment analysis 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. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61547469006 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.20 |
| title | Unsupervised Methods to Improve Aspect-Based Sentiment Analysis in Czech |
| topic | Computación Aspect latent semantics based sentiment analysis |
| url | https://www.redalyc.org/articulo.oa?id=61547469006 |