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

Year 2019, Volume: 7 Issue: 3, 467 - 472, 28.09.2019

Zekeriya Anıl Güven , Banu Diri , Tolgahan Çakaloğlu

https://doi.org/10.21541/apjes.459447

Cited By: 1

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. By using the multi-stages LDA, we were able to perform better (2-stages:70.5%, 3-stages:76.4%) than the state of the art result (60.4%) which was achieved using the plain LDA for 5 classes.

Keywords

Topic Modeling, Latent Dirichlet Allocation, Natural Language Processing, Emotion Analysis

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.

Keywords

Topic Modeling, Latent Dirichlet Allocation, Natural Language Processing, Emotion Analysis

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