A Combined CNN and LSTM Model for Arabic Sentiment Analysis

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

Deep neural networks have shown good data modelling capabilities when dealing with challenging and large datasets from a wide range of application areas. Convolutional Neural Networks (CNNs) offer advantages in selecting good features and Long Short-Term Memory (LSTM) networks have proven good abilities of learning sequential data. Both approaches have been reported to provide improved results in areas such image processing, voice recognition, language translation and other Natural Language Processing (NLP) tasks. Sentiment classification for short text messages from Twitter is a challenging task, and the complexity increases for Arabic language sentiment classification tasks because Arabic is a rich language in morphology. In addition, the availability of accurate pre-processing tools for Arabic is another current limitation, along with limited research available in this area. In this paper, we investigate the benefits of integrating CNNs and LSTMs and report obtained improved accuracy for Arabic sentiment analysis on different datasets. Additionally, we seek to consider the morphological diversity of particular Arabic words by using different sentiment classification levels.
LanguageEnglish
Title of host publicationCross Domain Conference for Machine Learning and Knowledge Extraction
Subtitle of host publicationCD-MAKE 2018
PublisherSpringer International Publishing
Pages179-191
Number of pages13
Volume11015
StatePublished - 24 Aug 2018

Publication series

NameLecture Notes in Computer Science

Fingerprint

Neural networks
Processing
Speech recognition
Data structures
Image processing
Availability
Long short-term memory
Deep neural networks

Cite this

Alayba, A., Palade, V., England, M., & Iqbal, R. (2018). A Combined CNN and LSTM Model for Arabic Sentiment Analysis. In Cross Domain Conference for Machine Learning and Knowledge Extraction: CD-MAKE 2018 (Vol. 11015, pp. 179-191). (Lecture Notes in Computer Science). Springer International Publishing.

A Combined CNN and LSTM Model for Arabic Sentiment Analysis. / Alayba, Abdulaziz; Palade, Vasile; England, Matthew; Iqbal, Rahat.

Cross Domain Conference for Machine Learning and Knowledge Extraction: CD-MAKE 2018. Vol. 11015 Springer International Publishing, 2018. p. 179-191 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Alayba, A, Palade, V, England, M & Iqbal, R 2018, A Combined CNN and LSTM Model for Arabic Sentiment Analysis. in Cross Domain Conference for Machine Learning and Knowledge Extraction: CD-MAKE 2018. vol. 11015, Lecture Notes in Computer Science, Springer International Publishing, pp. 179-191.
Alayba A, Palade V, England M, Iqbal R. A Combined CNN and LSTM Model for Arabic Sentiment Analysis. In Cross Domain Conference for Machine Learning and Knowledge Extraction: CD-MAKE 2018. Vol. 11015. Springer International Publishing. 2018. p. 179-191. (Lecture Notes in Computer Science).
Alayba, Abdulaziz ; Palade, Vasile ; England, Matthew ; Iqbal, Rahat. / A Combined CNN and LSTM Model for Arabic Sentiment Analysis. Cross Domain Conference for Machine Learning and Knowledge Extraction: CD-MAKE 2018. Vol. 11015 Springer International Publishing, 2018. pp. 179-191 (Lecture Notes in Computer Science).
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