  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.
