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Dergiler / Hittite Journal of Science and Engineering / 2020 / Cilt: 7 - Sayı: 3

Comparison of Neural Network Models for Nostalgic Sentiment Analysis of YouTube Comments

Seda Postalcioglu
Senem Aktas
Sayfa
215–221
Yayın Tarihi
2022-10-18
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

MLPNN, CNN, RNN-LSTM performance results are 78%,88%,88%, respectively

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:F or this study Sentiment Analysis SA is applied for the music comments using different Neural Network NN Models.
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Temel Bulgular & Veri:Their performances are analyzed.
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Sonuç & Katkı:MLPNN, CNN, RNN-LSTM performance results are 78%,88%,88%, respectively
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:CNN · MLPNN · NLP

Abstract

F or this study Sentiment Analysis SA is applied for the music comments using different Neural Network NN Models. SA is commonly used for Natural Language Processing NLP . With the help of NLP, the evaluations / tips about the future can be obtained by analyzing the correspondences and comments. The aim of the study is to draw conclusions from the comments made under the songs whether they are nostalgic. Data is captured using the YouTube Data API. Data extraction is done by entering the link of the song whose comments will be taken. CSV files are obtained and then labeled as nostalgic and non-nostalgic. Different neural network models as MLPNN Multi-Layer Perceptron Neural Network , CNN Convolutional Neural Network , RNN-LSTM Recurrent Neural Network-Long Short-Term Memory are applied for sentiment analysis. Their performances are analyzed. MLPNN, CNN, RNN-LSTM performance results are 78%,88%,88%, respectively

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Bu Makaleye Atıf Yap

Postalcioglu, S., Aktas, S. (2022). Comparison of Neural Network Models for Nostalgic Sentiment Analysis of YouTube Comments. Hittite Journal of Science and Engineering, Cilt: 7 - Sayı: 3, 215–221. https://ajindex.com/articles/537964

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