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- W2895547478 abstract "Abstract The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering, as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context." @default.
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- W2895547478 date "2019-03-01" @default.
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- W2895547478 title "Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review" @default.
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- W2895547478 doi "https://doi.org/10.1016/j.eswa.2018.10.003" @default.
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