Effective Twitter Sentiment Analysis Using Deep Belief Network with Enhanced Dragonfly Optimization Algorithm

Abstract

In this research article, an effective model is implemented for predicting the outcome of the Indian general election 2019 and farmer’s protest by utilizing the sentiment analysis of Twitter data. In the initial segment, the raw Twitter data are acquired from the Indian Political tweets 2019 and farmers protest tweets databases. Further, the denoising operations such as removal of unnecessary space, tabs, new-line, hashtag symbols, non-English characters, punctuations, numbers and special characters are used to enhance the quality of the acquired data. Then, the keyword trend analysis and topic modeling utilizing latent dirichlet allocations (LDAs) are performed for better data representation. Next, the extraction of the feature is carried out utilizing skip-gram and term frequency-document level frequency (TF-IDF) techniques, and further, the feature optimization is accomplished using enhanced dragonfly optimization algorithm (EDOA) for selecting the optimal feature vectors. In the EDOA, a Brownian motion is added for improving the probabilistic behaviors. Lastly, the selected features are given to the deep belief network (DBN) model to classify the people’s sentiments into negative, neutral, and positive classes. The experimental evaluations demonstrated that the EDOA-DBN model has obtained 99.22% and 98.83% of accuracy on both Indian political tweets 2019 and farmers protest tweets databases, which are maximum related to other existing models