Comparison of n-stage Latent Dirichlet Allocation versus other topic modeling methods for emotion analysis

Abstract

Understanding the emotions of sharing in social media plays a key role in learning people's thoughts. Knowing the emotion of human being with developing technology provides benefit in various fields. For example, media, marketing and advertising areas allow people to reflect on their use and idea specific content. In our study, Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA) and Probabilistic-Latent Semantic Analysis (P-LSA) were used to determine the emotions of individuals from Turkish tweets. In addition, the success of the developed n-stage state of the LDA algorithm in the emotion analysis was compared with the existing methods. The dataset consists of 4000 tweets of 5 different emotions, including angry, fear, happiness, sadness and surprise. All topic modeling methods were modeled for 3 and 5 class datasets and their successes and running times were measured. It has been observed that the developed n-stage LDA method achieves success in terms of running time and performance according to LDA and P-LSA. The most successful and fastest modeled method was LSA.