A Combined CNN and LSTM Model for Arabic Sentiment Analysis
arXiv:1807.02911 · doi:10.1007/978-3-319-99740-7_12
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.
Authors accepted version of submission for CD-MAKE 2018
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Cited by in corpus (3)
- Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis
- Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification
- Sentiment Analysis in Poems in Misurata Sub-dialect -- A Sentiment Detection in an Arabic Sub-dialect