# A Combined CNN and LSTM Model for Arabic Sentiment Analysis

arXiv: https://arxiv.org/abs/1807.02911 · HTML: https://mlsanity.com/paper/1807.02911/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [A Fair and Comprehensive Comparison of Multimodal Tweet Sentiment Analysis Methods](https://mlsanity.com/paper/2106.08829/) (2021)
- [SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm](https://mlsanity.com/paper/2301.02521/) (2023)
- [Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification](https://mlsanity.com/paper/2103.05683/) (2021)
- [Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis](https://mlsanity.com/paper/2112.00534/) (2021)
- [Content-Localization based System for Analyzing Sentiment and Hate Behaviors in Low-Resource Dialectal Arabic: English to Levantine and Gulf](https://mlsanity.com/paper/2312.03727/) (2023)
- [Sentiment Analysis in Poems in Misurata Sub-dialect -- A Sentiment Detection in an Arabic Sub-dialect](https://mlsanity.com/paper/2109.07203/) (2021)
- [Improving Clinical Efficiency and Reducing Medical Errors through NLP-enabled diagnosis of Health Conditions from Transcription Reports](https://mlsanity.com/paper/2206.13516/) (2022)

## Abstract (arXiv)

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.