Clustering Multilingual Aspect Phrases for Sentiment Analysis
| Autor: | Danny Suarez Vargas, Lucas Rafael Costella Pessutto, Viviane Pereira Moreira |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Sentiment analysis Feature extraction 02 engineering and technology computer.software_genre Task (project management) 020204 information systems 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Task analysis Unsupervised learning 020201 artificial intelligence & image processing Artificial intelligence Baseline (configuration management) Cluster analysis business computer Natural language processing |
| Zdroj: | WI |
| DOI: | 10.1109/wi.2018.00-91 |
| Popis: | The area of sentiment analysis has experienced significant developments in the last few years. More specifically, there has been growing interest in aspect-based sentiment analysis in which the goal is to extract, group, and rate the overall opinion about the features of the entity being evaluated. Techniques for aspect extraction can produce an undesirably large number of aspects - with many of those relating to the same product feature. This problem is aggravated when the reviews are written in many languages. In this paper, we address the novel task of multilingual aspect clustering which aims at grouping together the aspects extracted from reviews written in several languages. We contribute with a proposal of techniques to tackle this problem and test them on reviews written in five languages. Our experiments show that our unsupervised clustering technique achieves results that outperform a semi-supervised baseline in many cases. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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