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

Authors : Zekeriya Anıl GÜVEN, Banu DİRİ, Tolgahan ÇAKALOĞLU

Pages : 467-472

Doi:10.21541/apjes.459447

View : 11 | Download : 7

Publication Date : 2019-09-28

Article Type : Research Paper

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 insert ignore into journalissuearticles values(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. By using the multi-stages LDA, we were able to perform better insert ignore into journalissuearticles values(2-stages:70.5%, 3-stages:76.4%); than the state of the art result insert ignore into journalissuearticles values(60.4%); which was achieved using the plain LDA for 5 classes.
Keywords : Topic Modeling, Latent Dirichlet Allocation, Natural Language Processing, Emotion Analysis