期刊
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
卷 5, 期 6, 页码 292-303出版社
WILEY PERIODICALS, INC
DOI: 10.1002/widm.1171
关键词
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资金
- National High Technology Development 863 Program of China [2015AA015407]
- National Natural Science Foundation of China [61133012, 61273321]
- Baidu Fellowship
- IBM
Sentiment analysis (also known as opinion mining) is an active research area in natural language processing. It aims at identifying, extracting and organizing sentiments from user generated texts in social networks, blogs or product reviews. A lot of studies in literature exploit machine learning approaches to solve sentiment analysis tasks from different perspectives in the past 15 years. Since the performance of a machine learner heavily depends on the choices of data representation, many studies devote to building powerful feature extractor with domain expert and careful engineering. Recently, deep learning approaches emerge as powerful computational models that discover intricate semantic representations of texts automatically from data without feature engineering. These approaches have improved the state-of-the-art in many sentiment analysis tasks including sentiment classification of sentences/documents, sentiment extraction and sentiment lexicon learning. In this paper, we provide an overview of the successful deep learning approaches for sentiment analysis tasks, lay out the remaining challenges and provide some suggestions to address these challenges. WIREs Data Mining Knowl Discov 2015, 5:292-303. doi: 10.1002/widm.1171 For further resources related to this article, please visit the .
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