An incremental learning temporal influence model for identifying topical influencers on Twitter dataset

Sentiment analysis explores the views, perceptions and feelings of people concerning entities like subjects, goods, organizations, resources and individuals. The opinion of some people in social network influences the opinion behavior and thoughts of other people. They are known as influential user. In this article, both the sentiment analysis and identification of influential user are proposed. Initially, Twitter data are preprocessed by proposing weighted partition around medoids (WPAM) with artificial cooperative search (WPAM-ACS) which extracts topics from Twitter data through dynamic clustering (DC). For sentiment classification, NLP has been used in many works. The main issue of using NLP for sentiment classification is that many languages do not have the adequate resources to develop NLP models. So, a fuzzy deep neural network (FDNN) is proposed in this paper for sentiment classification, because FDNN effectively handles the uncertainties and noises in tweet data than other state of the arts. Emotional conformity is a metric that refers to how people from an emotional point of view agree with another person. It is given as additional input to FDNN along with the tweets for sentiment classification. Finally, influential users are detected by temporal influential model (TIM) formulated as likelihood function using incremental logistic regression (ILLR) in which user’s opinion sequence is considered for identification of influential user. In the experimental results, sentiment analysis is evaluated in terms of precision, recall and F-measure and proved that the proposed DC–FDNN sentiment classification is better than fixed clustering and NLP (FC–NLP)-based sentiment classification. Influential user detection using TIM-ILLR on opinion sequences which are identified by DC-FDNN is evaluated in terms of accuracy and proved that TIM-ILLR is better than other methods such as maximum likelihood estimation (MLE), support vector regression (SVR) and logistic regression (LR).