Unsupervised Methods to Improve Aspect-Based Sentiment Analysis in Czech



Document title: Unsupervised Methods to Improve Aspect-Based Sentiment Analysis in Czech
Journal: Computación y sistemas
Database: PERIÓDICA
System number: 000411056
ISSN: 1405-5546
Authors: 1
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Institutions: 1University of West Bohemia, Faculty of Applied Sciences, Plzen. República Checa
Year:
Season: Jul-Sep
Volumen: 20
Number: 3
Pages: 365-375
Country: México
Language: Inglés
Document type: Artículo
Approach: Experimental, aplicado
English abstract 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
Disciplines: Ciencias de la computación,
Literatura y lingüística
Keyword: Procesamiento de datos,
Lingüística aplicada,
Lingüística computacional,
Análisis de textos,
Semántica latente,
Análisis de sentimiento
Keyword: Computer science,
Literature and linguistics,
Data processing,
Applied linguistics,
Computing linguistics,
Text analysis,
Latent semantics,
Sentiment analysis
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