An intelligent sentiment prediction approach in social networks based on batch and streaming big data analytics using deep learning

In recent years, cloud computing applications have facilitated the distribution of heterogeneous, unstructured digitized data among users' social networks of varying opinions. Text processing at a large scale requires high-precision computational techniques, which increases the computational burden. The advent of big data analytics along with Natural Language Processing is a powerful factor in improving the efficiency of processing large-scale text data, using the kernel of the MapReduce big data analytics frameworks that allow parallelization of large computational operations. In this paper, we propose an intelligent sentiment prediction approach based on deep learning, batch, and streaming big data analytics. In fact, our main objective is to take advantages of the powerful tools provided by the distributed platforms, such as, Hadoop and Spark to preprocess streaming data. This involves various tasks such as cleaning the data, reducing its size, minimizing access time, and decreasing storage volume. This step prepares the big streaming data to be fully exploited by Deep Learning models. This work includes a research study on processing big data related to short volume scripts based on batch and streaming distributed frameworks as well as deep learning approaches in Natural Language Processing. We detail our idea for analyzing short texts to determine their semantic context and categorize them into pros and cons poles. There were different stages in building this model, the first involving data reduction and refinement using selected features and big data analysis tools. In the second stage, words are embedded by global vector to be computed in layers of convolutional and recurrent neural networks. The experimental study and the analysis of the results confirm the usefulness of our proposed model and its superiority over the main approaches studied in the literature. Our model achieved a performance of 96% in terms of accuracy.