文章基本信息

标题：Unsupervised Model for Aspect-Based Sentiment Analysis in Spanish
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作者：Carlos Henriquez ; Freddy Briceno ; Dixon Salcedo 等
期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2019
卷号：46
期号：3
页码：430-438
出版社：IAENG - International Association of Engineers
摘要：This paper presents an unsupervised model for Aspect-Based Sentiment Analysis in Spanish language, which automatically extracts the aspects of opinion and determines its associated polarity. The model uses ontologies for the detection of explicit and implicit aspects, and machine learning without supervision to determine the polarity of a grammatical structure in spanish. The unsupervised approach used, allows to implement a system quickly scalable to any language or domain. The experimental work was carried out using the dataset used in Semeval 2016 for Task 5 corresponding to Sentence-level ABSA. The implemented system obtained a 73.07 F1 value in the extraction of aspects and 84.8% accuracy in the sentiment classification. The system obtained the best results of all systems participating in the competition in the three issues mentioned above.
关键词：aspect based; ontology; sentiment analysis; unsupervised machine learning