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.
Abstract (translated)
深度神经网络在处理来自各种应用领域的具有挑战性的大型数据集时，已经展示了良好的数据建模功能。卷积神经网络（CNN）在选择良好特征方面具有优势，长期短期记忆（LSTM）网络已被证明具有良好的学习顺序数据的能力。据报道，这两种方法在诸如图像处理，语音识别，语言翻译和其他自然语言处理（NLP）任务的领域中提供了改进的结果。来自Twitter的短文本消息的情感分类是一项具有挑战性的任务，阿拉伯语情绪分类任务的复杂性增加，因为阿拉伯语是形态学中丰富的语言。此外，阿拉伯语的准确预处理工具的可用性是另一个当前的限制，以及该领域的有限研究。在本文中，我们研究了整合CNN和LSTM的好处，并报告了在不同数据集上获得的阿拉伯情绪分析的准确性提高。此外，我们试图通过使用不同的情感分类水平来考虑特定阿拉伯语单词的形态多样性。
URL
https://arxiv.org/abs/1807.02911
