Arabic sentiment analysis using dependency-based rules and deep neural networks

With the growth of social platforms in recent years and the rapid increase in the means of communication through these platforms, a significant amount of textual data is available that contains an abundance of individuals’ opinions. Sentiment analysis is a task that supports companies and organizations to evaluate this textual data with the intention of understanding people's thoughts concerning services or products. Most previous research in Arabic sentiment analysis relies on word frequencies, lexicons, or black box methods to determine the sentiment of a sentence. It should be noted that these approaches do not take into account the semantic relations and dependencies between words. In this work, we propose a framework that incorporates Arabic dependency-based rules and deep learning models. Dependency-based rules are created by using linguistic patterns to map the meaning of words to concepts in the dependency structure of a sentence. By examining the dependent words in a sentence, the general sentiment is revealed. In the first stage of sentiment classification, the dependency grammar rules are used. If the rules are unsuccessful in classifying the sentiment, the algorithm then applies deep neural networks (DNNs). Three DNN models were employed, namely LSTM, BiLSTM, and CNN, and several Arabic benchmark datasets were used for sentiment analysis. The performance results of the proposed framework show a greater improvement in terms of accuracy and F1 score and they outperform the state-of-the-art approaches in Arabic sentiment analysis.