Semantic similarity for aspect-based sentiment analysis

Pavel Blinov, Evgeniy V. Kotelnikov

Abstract: The paper investigates the problem of automatic aspect-based sentiment analysis. Such version is harder to do than general sentiment analysis, but it significantly pushes forward the limits of unstructured text analysis methods. In the beginning previous approaches and works are reviewed. That part also gives data description for train and test collections. In the second part of the article the methods for main subtasks of aspect-based sentiment analysis are described. The method for explicit aspect term extraction relies on the vector space of distributed representations of words. The term polarity detection method is based on use of pointwise mutual information and semantic similarity measure. Results from SentiRuEval workshop for automobiles and restaurants domains are given. Proposed methods achieved good results in several key subtasks. In aspect term polarity detection task and sentiment analysis of whole review on aspect categories methods showed the best result for both domains. In the aspect term categorization task our method was placed at the second position. And for explicit aspect term extraction the first result obtained for the restaurant domain according to partial match evaluation criteria.