文章基本信息

标题：A Contemporary Ensemble Aspect-based Opinion Mining Approach for Twitter Data
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作者：Satvika ; Vikas Thada ; Jaswinder Singh 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：5
页码：196
DOI：10.14569/IJACSA.2021.0120524
出版社：Science and Information Society (SAI)
摘要：Aspect-based opinion mining is one among the thought-provoking research field which focuses on the extraction of vivacious aspects from opinionated texts and polarity value associated with these. The principal aim here is to identify user sentiments about specific features of a product or service rather than overall polarity. This fine-grained polarity identification about myriad aspects of an entity is highly beneficial for individuals or business organizations. Extricating these implicit or explicit aspects can be very challenging and this paper elaborates copious aspect extraction techniques, which is decisive for aspect-based sentiment analysis. This paper presents a novel idea of combining several approaches like Part of Speech tagging, dependency parsing, word embedding, and deep learning to enrich the aspect-based sentiment analysis specially designed for Twitter data. The results show that combining deep learning with traditional techniques can produce excellent results than lexicon-based methods.
关键词：Aspect-based sentiment analysis; dependency parsing; long short-term memory (LSTM); part of speech (POS) tagging; term frequency-inverse document frequency (TF-IDF)