Emotion Detection with n-stage Latent Dirichlet Allocation for Turkish Tweets

Abstract

Understanding the reason behind the emotions placed in the social media plays a key role to learn mood characterization of any written texts that are not seen before. Knowing how to classify the mood characterization leads this technology to be useful in a variety of fields. The Latent Dirichlet Allocation (LDA), a topic modeling algorithm, was used to determine which emotions the tweets on Twitter had in the study. The dataset consists of 4000 tweets that are categorized into 5 different emotions that are anger, fear, happiness, sadness, and surprise. Zemberek, Snowball, and first 5 letters root extraction methods are used to create models. The generated models were tested by using the proposed n-stage LDA method. With the proposed method, we aimed to increase model's success rate by decreasing the number of words in the dictionary. Using the multi-stage LDA (2-stages:70.5%, 3-stages:76.375%) method, the success rate was increased compared to normal LDA (60.375%) for 5 class.

Impact of N-Stage Latent Dirichlet Allocation on Analysis of Headline Classification

“…As a feature extractor, LDA was chosen over other techniques since it provides a reliable representation of extensive text data in order to find different topics and their densities. In addition to the classical LDA (1-LDA), nstage LDA (n-LDA) [14] was also used in the pipeline to show its impact. The n-LDA method reduces the word count in a corpus, as it allows for the deletion of words that have less weight than the threshold value within the topics.…”