Using deep neural networks for extracting sentiment targets in arabic tweets

Ayman El-Kilany; Amr Azzam; Samhaa R. El-Beltagy

doi:10.1007/978-3-319-67056-0_1

Using deep neural networks for extracting sentiment targets in arabic tweets

Ayman El-Kilany
, Amr Azzam
, Samhaa R. El-Beltagy^*

^*Corresponding author for this work

Publication: Chapter in book/Conference proceeding › Chapter in edited volume

Abstract

In this paper, we investigate the problem of recognizing entities which are targeted by text sentiment in Arabic tweets. To do so, we train a bidirectional LSTM deep neural network with conditional random fields as a classification layer on top of the network to discover the features of this specific set of entities and extract them from Arabic tweets. We’ve evaluated the network performance against a baseline method which makes use of a regular named entity recognizer and a sentiment analyzer. The deep neural network has shown a noticeable advantage in extracting sentiment target entities from Arabic tweets.

Original language	English
Title of host publication	Intelligent natural language processing
Subtitle of host publication	trends and applications
Editors	Khaled Shaalan, Aboul Ella Hassanien, Fahmy Tolba
Place of Publication	Cham
Publisher	Springer
Pages	3-15
Number of pages	13
ISBN (Electronic)	978-3-319-67056-0
ISBN (Print)	978-3-319-67055-3
DOIs	https://doi.org/10.1007/978-3-319-67056-0_1
Publication status	Published - 2018
Externally published	Yes

Publication series

Series	Studies in Computational Intelligence
Volume	740
ISSN	1860-949X

Bibliographical note

Publisher Copyright:
© 2018, Springer International Publishing AG.

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10.1007/978-3-319-67056-0_1

Cite this

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language = "English",

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Using deep neural networks for extracting sentiment targets in arabic tweets. / El-Kilany, Ayman; Azzam, Amr; El-Beltagy, Samhaa R.
Intelligent natural language processing: trends and applications. ed. / Khaled Shaalan; Aboul Ella Hassanien; Fahmy Tolba. Cham: Springer, 2018. p. 3-15 (Studies in Computational Intelligence, Vol. 740).

Publication: Chapter in book/Conference proceeding › Chapter in edited volume

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AU - Azzam, Amr

AU - El-Beltagy, Samhaa R.

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Y1 - 2018

N2 - In this paper, we investigate the problem of recognizing entities which are targeted by text sentiment in Arabic tweets. To do so, we train a bidirectional LSTM deep neural network with conditional random fields as a classification layer on top of the network to discover the features of this specific set of entities and extract them from Arabic tweets. We’ve evaluated the network performance against a baseline method which makes use of a regular named entity recognizer and a sentiment analyzer. The deep neural network has shown a noticeable advantage in extracting sentiment target entities from Arabic tweets.

AB - In this paper, we investigate the problem of recognizing entities which are targeted by text sentiment in Arabic tweets. To do so, we train a bidirectional LSTM deep neural network with conditional random fields as a classification layer on top of the network to discover the features of this specific set of entities and extract them from Arabic tweets. We’ve evaluated the network performance against a baseline method which makes use of a regular named entity recognizer and a sentiment analyzer. The deep neural network has shown a noticeable advantage in extracting sentiment target entities from Arabic tweets.

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Using deep neural networks for extracting sentiment targets in arabic tweets

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