A Combined CNN and LSTM Model for Arabic Sentiment Analysis

Abdulaziz Alayba, Vasile Palade, Matthew England, Rahat Iqbal

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

    132 Citations (Scopus)
    213 Downloads (Pure)

    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.
    Original 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
    ISBN (Electronic)978-3-319-99740-7
    ISBN (Print)978-3-319-99739-1
    DOIs
    Publication statusPublished - 24 Aug 2018
    EventIFIP Cross Domain Conference for Machine Learning and Knowledge Extraction - Hamburg, Germany
    Duration: 27 Aug 201830 Aug 2018

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    ISSN (Print)0302-9743

    Conference

    ConferenceIFIP Cross Domain Conference for Machine Learning and Knowledge Extraction
    Abbreviated titleCD-MAKE 2018
    Country/TerritoryGermany
    CityHamburg
    Period27/08/1830/08/18

    Fingerprint

    Dive into the research topics of 'A Combined CNN and LSTM Model for Arabic Sentiment Analysis'. Together they form a unique fingerprint.

    Cite this