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Dergiler / Academic Platform Journal of Engineering and Smart Systems / 2019 / Volume: 7 Issue: 3

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

Zekeriya Anıl Güven(COMPUTER ENGINEERING (DR))0000-0002-7025-2815
Banu Diri(DEPARTMENT OF COMPUTER ENGINEERING)0000-0002-4052-0049
Tolgahan Çakaloğlu(University of Arkansas, Computer Science and Computer Engineering Department)0000-0002-4711-7287
Sayfa
—
Yayın Tarihi
2019-09-28
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez: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.
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Temel Bulgular & Veri:The dataset consists of 4000 tweets that are categorized into 5 different emotions that are anger, fear, happiness, sadness, and surprise.
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Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Topic Modeling · Latent Dirichlet Allocation · Natural Language Processing

Özet

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.

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.

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Bu Makaleye Atıf Yap

Güven, Z. A., Diri, B., Çakaloğlu, T. (2019). Emotion Detection with n-stage Latent Dirichlet Allocation for Turkish Tweets. Academic Platform Journal of Engineering and Smart Systems, Volume: 7 Issue: 3. https://doi.org/10.21541/apjes.459447

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