An Integrated Deep Learning with Natural Language Processing Models for Sentiment Analysis and Classification using Arabic Tweets

The growing acceptance of social media networks as a platform to share opinions on several features emerged opinion mining or sentiment analysis (SA) as an active investigation part. In recent times, SA has attracted significant attention owing to its various applications in different features of our lives. SA is one of the Natural Language Processing (NLP) that purposes to analyze and process data that is transcribed in human languages. Even though the Arabic language is the most extensively spoken language utilized for content sharing through social media, the SA on Arabic content is restricted owing to numerous challenges with the language’s morphologic structures, the dialect's variabilities, and the absence of the proper corpora. In recent times, deep learning (DL) and machine learning (ML) have demonstrated extraordinary achievements in the field of SA for Arabic tweet classification in social media platforms. In this manuscript, we design and develop an Integrated Deep Learning with Natural Language Processing Models for Sentiment Analysis and Classification (IDLNLPM-SAC) technique. The IDLNLPM-SAC model presents a sentiment analysis and classification using Arabic tweets. The presented IDLNLPM-SAC model follows different levels of data preprocessing to transform the raw Arabic tweet data into a compatible format. For the process of word embedding, the latent semantic analysis (LSA) technique can be deployed. Besides, the hybrid of parallel temporal convolutional network–gated recurrent unit (PTCN-GRU) classifier can be implemented for the classification process. Eventually, the parameter choice of the PTCNGRU algorithm can be implemented by the design of the improved marine predator algorithm (IMPA). The simulation evaluation of the IDLNLPM-SAC technique takes place using the Arabic tweets database. The experimental results pointed out the heightened solution of the IDLNLPM-SAC technique compared to recent approaches.