A Quality Table-Based Method for Sentiment Expression Word Identification in Japanese
Identifying and summarizing opinions from online reviews is a valuable and challenging task and aspect-level sentiment analysis is a research-based approach to this task. Sentiment expression word identification is important sentiment identification task since many unique expression words appear in each entity domain and it is confirmed that text data from the internet has many collateral expressions. Generally, syntax-based model is applied to sentiment expression word identification method. Syntax-based model can consider low frequency word; however, we need to consider many syntax relations and that may be not practical. Therefore, it is difficult to identify sentiment expression words with syntax-based model. This paper proposes quality table-based method for sentiment expression word identification. The method identifies sentiment expression words with supervised learning. The training set is created with both seed expression-aspect and word-aspect deployment based on characteristic of quality table’s relation. This paper proposes a non-syntax and relation-based model in order to solve syntax-based models’ problems. This paper carries out an experimental test, demonstrates how many unique SEWs are extracted, and verifies the coverage of SEW with annotated text.
KeywordsAspect-level sentiment analysis Sentiment identification Customer review Quality table Supervised learning
In this research, we used Rakuten market review data provided by Rakuten Inc. and the National Institute of Informatics. The authors would like to thank them.
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