Systematic Literature Review for Sentiment Analysis Using Big Data social media Streams

Social media is a potential tool for people to communicate with each other remotely and express their opinions and feelings about any subject of concern. This behaviour generates a voluminous amount of unstructured data. Profitable business and research models can be developed by leveraging this data. Therefore, several machine learning and NLP-based algorithms have been developed in recent decades, to analyse people’s opinion and their attitude on any subject of concern. Deep learning-based approaches are gaining a lot of traction because of their excellent performance. The domain of sentiment analysis has witnessed the deployment of various machine learning, deep learning, and optimization-based models. This work unveils the prospects of these cutting-edge technologies with extensive analysis. In addition to this, the survey includes a detail of the most popular datasets, with their characteristics, along with maximum accuracy attained. This pivotal goal of the work is to appreciate and assess the effectiveness of deep learning architectures in the domain of sentiment analysis. This work also studies the usage of optimization-based method in improving the results of learning models.