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

Seda POSTALCİOGLU; Senem AKTAS

doi:https://doi.org/10.17350/HJSE19030000191

Makale detayları

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

Yazarlar:

Seda POSTALCİOGLU | Senem AKTAS

Yayıncı:

Hitit University

Dergi:

Hittite Journal of Science and Engineering 2020 Vol.7 Issue 3, pp.215-221

Belge Tipi:

Makale

Yayın Tarihi:

9/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000451408

Sıklık:

Üç Aylık

eISSN:

2148-4171

DOI:

https://doi.org/10.17350/HJSE19030000191

Özet:

 

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.

Makale detayları

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

Yazarlar: Seda POSTALCİOGLU | Senem AKTAS

Yayıncı: Hitit University

Dergi: Hittite Journal of Science and Engineering 2020 Vol.7 Issue 3, pp.215-221

Belge Tipi: Makale

Yayın Tarihi: 9/2020

Belge Dili: İngilizce

Document ID (Org No.): 2000451408

Sıklık: Üç Aylık

eISSN: 2148-4171

DOI: https://doi.org/10.17350/HJSE19030000191

Sayı

Yazarlar:

Seda POSTALCİOGLU | Senem AKTAS

Yayıncı:

Hitit University

Dergi:

Hittite Journal of Science and Engineering 2020 Vol.7 Issue 3, pp.215-221

Belge Tipi:

Makale

Yayın Tarihi:

9/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000451408

Sıklık:

Üç Aylık

eISSN:

2148-4171

DOI:

https://doi.org/10.17350/HJSE19030000191