Abstract
With the outbreak of social networks, blogs, and forums, classifying subjective text influenced by personal feelings and opinions has become an interesting research area. Many techniques have been proposed to solve the problem of analyzing and classifying sentiments held in those reviews and recommendations. Recently, deep learning models showed promising outcomes in many fields, including sentiment analysis. Therefore in this study, we propose a sentiment analysis deep learning-based model to predict the polarity of opinions and sentiments. Two types of recurrent neural networks are leveraged to learn higher-level representations. Then to mitigate the data dependency problem and to increase the model robustness, three distinct classification algorithms were utilized to produce the final output. Experimental results proved that our model prevailed in all the selected datasets with an accuracy ranging between 81.11 and 94.32%. Moreover, the model reduced the relative classification error rate by up to 26% compared to state-of-the-art models.