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Implicit opinion analysis: Extraction and polarity labelling
Author(s) -
Huang HenHsen,
Wang JunJie,
Chen HsinHsi
Publication year - 2017
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23835
Subject(s) - labelling , polarity (international relations) , computer science , sentiment analysis , natural language processing , information retrieval , extraction (chemistry) , artificial intelligence , psychology , chemistry , chromatography , biochemistry , criminology , cell
Opinion words are crucial information for sentiment analysis. In some text, however, opinion words are absent or highly ambiguous. The resulting implicit opinions are more difficult to extract and label than explicit ones. In this paper, cutting‐edge machine‐learning approaches – deep neural network and word‐embedding – are adopted for implicit opinion mining at the snippet and clause levels. Hotel reviews written in Chinese are collected and annotated as the experimental data set. Results show the convolutional neural network models not only outperform traditional support vector machine models, but also capture hidden knowledge within the raw text. The strength of word‐embedding is also analyzed.