Deep learning for sentiment analysis: successful approaches and future challenges

TLDR

Sentiment analysis, an active NLP field, seeks to identify and organize opinions from user‑generated texts, with past work relying on engineered features and recent deep learning methods automatically learning semantic representations. This paper reviews successful deep learning methods for sentiment analysis, outlines remaining challenges, and proposes suggestions to address them. The authors survey state‑of‑the‑art deep learning architectures applied to sentiment classification, extraction, and lexicon learning, and discuss their implementation details. Deep learning approaches have advanced state‑of‑the‑art performance across sentiment classification, extraction, and lexicon learning tasks. Published in WIREs Data Mining Knowledge Discovery 2015, 5:292–303 (doi:10.1002/widm.1171).

Abstract

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