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WIREs Data Mining Knowl Discov
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Deep learning for sentiment analysis: successful approaches and future challenges

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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 This article is categorized under: Algorithmic Development > Text Mining Technologies > Classification Technologies > Machine Learning
Neural models for learning sentiment‐specific word embeddings. The hybrid prediction model is proposed by Tang et al. and the hybrid ranking model is introduced by Tang et al.
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A classification approach leveraging word embedding for building sentiment lexicon.
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An illustration of recurrent neural network for sequential labeling.
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A brief illustration about convolutional neural network and recursive neural network.
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Supervised framework for sentiment classification of sentences/documents.
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Technologies > Classification
Technologies > Machine Learning
Algorithmic Development > Text Mining

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