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Erschienen in:
Sentence Compression with Reinforcement Learning Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Attention Aware Bidirectional Gated Recurrent Unit Based Framework for Sentiment Analysis

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

Erschienen in: Knowledge Science, Engineering and Management

Verlag: 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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Vorheriges Kapitel Distant Domain Adaptation for Text Classification
Nächstes Kapitel Neural Sentiment Classification with Social Feedback Signals
Fußnoten
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Metadaten
Titel
Attention Aware Bidirectional Gated Recurrent Unit Based Framework for Sentiment Analysis
verfasst von
Zhengxi Tian
Wenge Rong
Libin Shi
Jingshuang Liu
Zhang Xiong
Copyright-Jahr
2018
Buch
Knowledge Science, Engineering and Management

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

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

Copyright-Jahr: 2018

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

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