Intelligent Deep Neural Network integrated with Chaotic Particle Swarm Intelligence based Sentiment Analysis in Big Data Paradigm

摘要

In big data paradigm usage of sentiment analysis has rapidly increased its commendable pace in any kind of environment like political, social or present affairs. As it is easy to gather the sentiments of public of entire world are stated through the assistance of social media which is more suitable for sentiment mining. This paper focuses on analyzing the service of airline industries, by applying the fuzzy induced Intelligent deep neural network (IDNN) empowered with the knowledge of chaotic particle swarm optimization, which uses the twitter sentiment analysis on their respective passengers to get their feedback or opinion. This work used the deep learning model with the fuzzy control for fine tuning the weight assignment of the DNN more precisely. The tweets are extracted and they are preprocessed to extract the essential features involved in classification of the sentiments as either positive, negative or neutral. The fuzzy deep neural network is fine-tuned by integrating chaotic particle swarm optimization. The particle swarm behaviour improves the performance of the Fuzzy DNN by avoiding random selection of population to chaotic dynamics. The performance of the developed IDNN is compared with the support vector machine and random forest classifier. The results show that the proposed model well handles with imbalance voluminous dataset in big data paradigm with higher accuracy rate.