A Combined CNN and LSTM Model for Arabic Sentiment Analysis

TL;DR

This paper proposes a combined CNN and LSTM deep learning model to improve Arabic sentiment analysis, addressing language complexity and limited resources, and demonstrates enhanced accuracy on various datasets.

Contribution

It introduces an integrated CNN-LSTM approach specifically tailored for Arabic sentiment analysis, considering morphological diversity and multiple classification levels.

Findings

Improved accuracy over existing models on Arabic sentiment datasets

Effective handling of Arabic morphological complexity

Demonstrated benefits of combining CNN and LSTM for NLP tasks

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.