BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 7 Sayı: 3, 215 - 221, 30.09.2020
https://doi.org/10.17350/HJSE19030000191

Öz

Kaynakça

  • 1. Kumar A, Rani R. Sentiment analysis using neural network, 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun,pp. 262-267, 2016.
  • 2. Ayutthaya T S N, Pasupa K. Thai Sentiment Analysis via Bidirectional LSTM-CNN Model with Embedding Vectors and Sentic Features. International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), Pattaya, Thailand, pp. 1-6, 2018.
  • 3. Goularas D, Kamis S. Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data. International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), Istanbul, Turkey, pp. 12- 17, 2019.
  • 4. http://www.officialcharts.com/charts/singles-chart/, Acces date:02/01/2020
  • 5. Bhoir S, Ghorpade T, Mane V. Comparative analysis of different word embedding models. International Conference on Advances in Computing, Communication and Control (ICAC3), Mumbai, pp. 1-4, 2017.
  • 6. Huynh T, Le A. Integrating Grammatical Features into CNN Model for Emotion Classification. 5th NAFOSTED Conference on Information and Computer Science (NICS), Ho Chi Minh City, pp. 243-249, 2018.
  • 7. Kamran K, Meimandi J, Heidarysafa K, Mendu M, Barnes S, Brown L, Id D, Barnes L. Text Classification Algorithms: A Survey. Information (Switzerland), 10 (2019) 1-68.
  • 8. Wan F. Sentiment Analysis of Weibo Comments Based on Deep Neural Network. International Conference on Communications, Information System and Computer Engineering (CISCE), Haikou, China, pp. 626-630, 2019.
  • 9. Brownlee J. https://machinelearningmastery.com/ difference-between-a-batch-and-an-epoch/ Accesed on: 20.11.2019
  • 10. Lu Y, Shi Y, Jia G, and Yang J. A new method for semantic consistency verification of aviation radiotelephony communication based on LSTM-RNN. IEEE International Conference on Digital Signal Processing (DSP), Beijing, pp. 422-426, 2016.
  • 11. Timoney J, Raj A, Davis B. Nostalgic Sentiment Analysis of YouTube Comments for Chart Hits of the 20th Century. 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science, AICS, pp:1-10, 2018.
  • 12. Xiong H, Liu Q, Song S, Cai Y. Region-based convolutional neural network using group sparse regularization for image sentiment classification. Journal on Image and Video Proc. 30 (2019), pp. 2-9.
  • 13. Ripley B. Feed-forward Neural Networks. Pattern Recognition and Neural Networks Cambridge: Cambridge University Press, pp. 143-180, 1996. doi:10.1017/ CBO9780511812651.006
  • 14. Huang X. Convolutional Neural Networks In Convolution, https://www.researchgate.net/publication/328189519_ Convolutional_Neural_Networks_In_Convolution. Accesed date:18.06.2020.
  • 15. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), pp.2278–2324, 1998. doi:10.1109/5.726791
  • 16. Kingma D, Ba J. Adam: A method for stochastic optimization. ICLR Conference, pp:1-15,2015.
  • 17. Kathuria A, Intro to optimization in deep learning: Momentum, RMSProp and Adam, https://blog.paperspace. com/intro-to-optimization-momentum-rmsprop-adam/ (Visited:01.09.2020)
  • 18. Wang Z, Bovik A. Mean squared error: love it or leave it? - A new look at signal fidelity measures. Signal Processing Magazine, IEEE. 26 (2009) 98 - 117. 10.1109/ MSP.2008.930649.
  • 19. Powers D, Ailab. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2 (2011),pp. 2229- 3981. 10.9735/2229-3981.

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

Yıl 2020, Cilt: 7 Sayı: 3, 215 - 221, 30.09.2020
https://doi.org/10.17350/HJSE19030000191

Öz

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

Kaynakça

  • 1. Kumar A, Rani R. Sentiment analysis using neural network, 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun,pp. 262-267, 2016.
  • 2. Ayutthaya T S N, Pasupa K. Thai Sentiment Analysis via Bidirectional LSTM-CNN Model with Embedding Vectors and Sentic Features. International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), Pattaya, Thailand, pp. 1-6, 2018.
  • 3. Goularas D, Kamis S. Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data. International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), Istanbul, Turkey, pp. 12- 17, 2019.
  • 4. http://www.officialcharts.com/charts/singles-chart/, Acces date:02/01/2020
  • 5. Bhoir S, Ghorpade T, Mane V. Comparative analysis of different word embedding models. International Conference on Advances in Computing, Communication and Control (ICAC3), Mumbai, pp. 1-4, 2017.
  • 6. Huynh T, Le A. Integrating Grammatical Features into CNN Model for Emotion Classification. 5th NAFOSTED Conference on Information and Computer Science (NICS), Ho Chi Minh City, pp. 243-249, 2018.
  • 7. Kamran K, Meimandi J, Heidarysafa K, Mendu M, Barnes S, Brown L, Id D, Barnes L. Text Classification Algorithms: A Survey. Information (Switzerland), 10 (2019) 1-68.
  • 8. Wan F. Sentiment Analysis of Weibo Comments Based on Deep Neural Network. International Conference on Communications, Information System and Computer Engineering (CISCE), Haikou, China, pp. 626-630, 2019.
  • 9. Brownlee J. https://machinelearningmastery.com/ difference-between-a-batch-and-an-epoch/ Accesed on: 20.11.2019
  • 10. Lu Y, Shi Y, Jia G, and Yang J. A new method for semantic consistency verification of aviation radiotelephony communication based on LSTM-RNN. IEEE International Conference on Digital Signal Processing (DSP), Beijing, pp. 422-426, 2016.
  • 11. Timoney J, Raj A, Davis B. Nostalgic Sentiment Analysis of YouTube Comments for Chart Hits of the 20th Century. 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science, AICS, pp:1-10, 2018.
  • 12. Xiong H, Liu Q, Song S, Cai Y. Region-based convolutional neural network using group sparse regularization for image sentiment classification. Journal on Image and Video Proc. 30 (2019), pp. 2-9.
  • 13. Ripley B. Feed-forward Neural Networks. Pattern Recognition and Neural Networks Cambridge: Cambridge University Press, pp. 143-180, 1996. doi:10.1017/ CBO9780511812651.006
  • 14. Huang X. Convolutional Neural Networks In Convolution, https://www.researchgate.net/publication/328189519_ Convolutional_Neural_Networks_In_Convolution. Accesed date:18.06.2020.
  • 15. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), pp.2278–2324, 1998. doi:10.1109/5.726791
  • 16. Kingma D, Ba J. Adam: A method for stochastic optimization. ICLR Conference, pp:1-15,2015.
  • 17. Kathuria A, Intro to optimization in deep learning: Momentum, RMSProp and Adam, https://blog.paperspace. com/intro-to-optimization-momentum-rmsprop-adam/ (Visited:01.09.2020)
  • 18. Wang Z, Bovik A. Mean squared error: love it or leave it? - A new look at signal fidelity measures. Signal Processing Magazine, IEEE. 26 (2009) 98 - 117. 10.1109/ MSP.2008.930649.
  • 19. Powers D, Ailab. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2 (2011),pp. 2229- 3981. 10.9735/2229-3981.
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Research Article
Yazarlar

Seda Postalcioglu Bu kişi benim

Senem Aktas Bu kişi benim

Yayımlanma Tarihi 30 Eylül 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 7 Sayı: 3

Kaynak Göster

Vancouver Postalcioglu S, Aktas S. Comparison of Neural Network Models for Nostalgic Sentiment Analysis of YouTube Comments. Hittite J Sci Eng. 2020;7(3):215-21.

Hittite Journal of Science and Engineering Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.