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Deep Learning in Sentiment Analysis

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Deep Learning in Natural Language Processing

Abstract

Sentiment analysis (also known as opinion mining) is an active research area in natural language processing. The task aims at identifying, extracting, and organizing sentiments from user-generated texts in social networks, blogs, or product reviews. Over the past two decades, many studies in the literature exploit machine learning approaches to solve sentiment analysis tasks from different perspectives. 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 expertise 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, opinion extraction, fine-grained sentiment analysis, etc. In this paper, we give an overview of the successful deep learning approaches sentiment analysis tasks at different levels.

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Notes

  1. 1.

    https://twitter.com/.

  2. 2.

    https://www.facebook.com.

  3. 3.

    http://www.imdb.com/.

  4. 4.

    https://www.amazon.com/.

  5. 5.

    https://www.yelp.com/.

  6. 6.

    https://code.google.com/p/word2vec/.

  7. 7.

    In practice, it is time consuming to obtain the document- level sentiment labels via human annotation. Researchers typically leverage the review documents from IMDB, Amazon, and Yelp, and regard the associated rating stars as the sentiment labels.

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  1. Microsoft Research Asia, Beijing, China

    Duyu Tang

  2. Heilongjiang University, Harbin, Heilongjiang, China

    Meishan Zhang

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  1. AI Research at Citadel , Chicago, Illinois, USA

    Li Deng

  2. Tsinghua University , Beijing, China

    Yang Liu

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Tang, D., Zhang, M. (2018). Deep Learning in Sentiment Analysis. In: Deng, L., Liu, Y. (eds) Deep Learning in Natural Language Processing. Springer, Singapore. https://doi.org/10.1007/978-981-10-5209-5_8

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