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Sentence Compression with Reinforcement Learning Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Attention Aware Bidirectional Gated Recurrent Unit Based Framework for Sentiment Analysis

Authors : Zhengxi Tian, Wenge Rong, Libin Shi, Jingshuang Liu, Zhang Xiong

Published in: Knowledge Science, Engineering and Management

Publisher: Springer International Publishing

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Abstract

Sentiment analysis is an effective technique and widely employed to analyze sentiment polarity of reviews and comments on the Internet. A lot of advanced methods have been developed to solve this task. In this paper, we propose an attention aware bidirectional GRU (Bi-GRU) framework to classify the sentiment polarity from the aspects of sentential-sequence modeling and word-feature seizing. It is composed of a pre-attention Bi-GRU to incorporate the complicated interaction between words by sentence modeling, and an attention layer to capture the keywords for sentiment apprehension. Afterward, a post-attention GRU is added to imitate the function of decoder, aiming to extract the predicted features conditioned on the above parts. Experimental study on commonly used datasets has demonstrated the proposed framework’s potential for sentiment classification.

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previous chapter Distant Domain Adaptation for Text Classification
next chapter Neural Sentiment Classification with Social Feedback Signals
Footnotes
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Metadata
Title
Attention Aware Bidirectional Gated Recurrent Unit Based Framework for Sentiment Analysis
Authors
Zhengxi Tian
Wenge Rong
Libin Shi
Jingshuang Liu
Zhang Xiong
Copyright Year
2018
Book
Knowledge Science, Engineering and Management

Print ISBN: 978-3-319-99364-5

Electronic ISBN: 978-3-319-99365-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-99365-2_6

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