Comparison Method for Emotion Detection of Twitter Users

Abstract

The development of technology has enabled the use of new ways and methods to determine the emotion of sharing on social media. For areas such as media and advertising, social media plays an important role today. In this study, Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) methods in subject modeling were used to determine the emotions of tweets thrown through Twitter. In addition, the model was also supported by an LDA-based method to increase the success of the system. The dataset consists of 5 emotions; angry, fear, happy, sadness and surprised. The success of all topic modeling methods used in the study was measured and most successful method was NMF. Then, success of the machine learning algorithms were measured by creating file according to Weka with word weights and class label of the topics. The most successful method was nstage LDA while the most successful algorithm was Random Forest.