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Journals / ACADEMIC PLATFORM-JOURNAL OF ENGINEERING AND SCIENCE / 2019 / Cilt: 7 - Sayı: 3

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

Zekeriya Anıl Güven
Tolgahan Cakaloğlu
Banu Diri
Pages
467–472
Publication Date
2022-10-11
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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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Key Findings & Data:The dataset consists of 4000 tweets that are categorized into 5 different emotions that are anger, fear, happiness, sadness, and surprise.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

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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Cite This Article

Güven, Z. A., Cakaloğlu, T., Diri, B. (2022). Emotion Detection with n-stage Latent Dirichlet Allocation for Turkish Tweets. ACADEMIC PLATFORM-JOURNAL OF ENGINEERING AND SCIENCE, Cilt: 7 - Sayı: 3, 467–472. https://ajindex.com/articles/427262

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