A turbulent flow optimized deep fused ensemble model (TFO-DFE) for sentiment analysis using social corpus data

Social media sites are a popular medium for interaction in the modern, expanding globe, where everyone has a connection with social media in some way. The people are accustomed to reading reviews before making decisions, for instance reading comments for movies, eateries, online stores, and a variety of other products. Taking reviews entails being aware of what other people think. It can be described in one way as sentiment analysis or even as opinion mining. For correctly predicting sentiments from social corpus data, the turbulent flow optimized deep fused ensemble model is a novel and sophisticated approach for sentiment analysis. The preprocessed data were used to extract a variety of features, including bag of words, term frequency-inverse term frequency, word to vector, and Glove. Then, the contemporary turbulent flow of water-based optimization mechanism was used to select the features that would be most useful for training the classifier. In addition, the cutting-edge deep fused ensemble voting classifier is employed to construct a precise decision function in accordance with the average probability of the number of classifiers. This work uses the well-known benchmarking social corpus datasets from IMDB, Twitter, Airlines, Amazon, Crowded Flower, and Apple for system analysis and study.