Cross-Event User Reaction Prediction on a Social Network Platform

Social network surges with multiple tweets with mixture of multiple emotions by many users when events like rape, robbery, war and murder, we use this user data to analyze user emotions between cross-events and try to predict user reactions for the next possible such event. Cross-events are a series of events that belong under the same umbrella of topics and are related to the events occurring prior to it. The proposed system solve this problem using collaborative filtering using Topical and Social context. The Text Rank Algorithm is an unsupervised algorithm used for keyword extraction. Count Vectorizer is used on preprocessed text to get the frequency of words throughout the text which is used as training data to get a probability of emotion using a logistic regression model. We incorporated social context along with topical context to account for homophily and used the Low-rank matrix factorization method for user-topic prediction. The model as an output gives a total of 8 emotions which include Shame, Disgust, Anger, Fear, Sadness, Neutral, Surprise and Joy. Finally, the model is able to predict emotions with an accuracy of 95% considering cross events.