A Combined CNN and LSTM Model for Arabic Sentiment Analysis

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.Comment: Authors accepted version of submission for CD-MAKE 201

Arabic Sentiment Analysis with Noisy Deep Explainable Model

Sentiment Analysis (SA) is an indispensable task for many real-world applications. Compared to limited resourced languages (i.e., Arabic, Bengali), most of the research on SA are conducted for high resourced languages (i.e., English, Chinese). Moreover, the reasons behind any prediction of the Arabic sentiment analysis methods exploiting advanced artificial intelligence (AI)-based approaches are like black-box - quite difficult to understand. This paper proposes an explainable sentiment classification framework for the Arabic language by introducing a noise layer on Bi-Directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN)-BiLSTM models that overcome over-fitting problem. The proposed framework can explain specific predictions by training a local surrogate explainable model to understand why a particular sentiment (positive or negative) is being predicted. We carried out experiments on public benchmark Arabic SA datasets. The results concluded that adding noise layers improves the performance in sentiment analysis for the Arabic language by reducing overfitting and our method outperformed some known state-of-the-art methods. In addition, the introduced explainability with noise layer could make the model more transparent and accountable and hence help adopting AI-enabled system in practice.Comment: This is the pre-print version of our accepted paper at the 7th International Conference on Natural Language Processing and Information Retrieval~(ACM NLPIR'2023

A hybrid approach based on personality traits for hate speech detection in Arabic social media

In recent years, as social media has grown in popularity, people have gained the ability to freely share their views. However, this may lead to users' conflict and hostility, resulting in unattractive online environments. Hate speech relates to using expressions or phrases that are violent, offensive, or insulting to a minority of people. The number of Arab social media users is quickly rising, and this is being followed by an increase in the frequency of cyber hate speech in the area. Therefore, the automated detection of Arabic hate speech has become a major concern for many stakeholders. The intersection of personality learning and hate speech detection is a relatively less studied niche. We suggest a novel approach that is focused on extracting personality trait features and using these features to detect Arabic hate speech. The experimental results show that the proposed approach is superior in terms of the macro-F1 score by achieving 82.3% compared to previous work reported in the literature