Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 2.111

Deep learning for sentiment analysis: successful approaches and future challenges

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

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

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.
[ Normal View | Magnified View ]
A classification approach leveraging word embedding for building sentiment lexicon.
[ Normal View | Magnified View ]
An illustration of recurrent neural network for sequential labeling.
[ Normal View | Magnified View ]
A brief illustration about convolutional neural network and recursive neural network.
[ Normal View | Magnified View ]
Supervised framework for sentiment classification of sentences/documents.
[ Normal View | Magnified View ]

Browse by Topic

Technologies > Machine Learning
Algorithmic Development > Text Mining
Technologies > Classification

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts