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Performing Sentiment Analysis with the Proposed Artificial Intelligence-Based Method Using Movie Commentaries

Yıl 2022, Cilt: 34 Sayı: 2, 751 - 760, 30.09.2022
https://doi.org/10.35234/fumbd.1138128

Öz

With the developing technology, social media, forum sites and blocks have been widely used. People are now using these channels very widely and they share their feelings and thoughts in these environments. Therefore, natural language processing applications have started to become a more popular topic with each passing day. One of the most popular topics in natural language processing is sentiment analysis. In sentiment analysis, subjective information is extracted by making examinations according to certain criteria. In this study, the IMDB data set was used to perform sentiment analysis. The IMDB dataset is one of the largest datasets on this subject, consisting of movie reviews. This dataset contains users' comments about movies. In the study, firstly, the data preprocessing step was carried out. Then, the prepared data set was classified in classical machine learning classifiers and the proposed ESA-based model. The proposed ESA-based model was more successful than the classical machine learning classifiers in analyzing the texts in the IMDB dataset, and the proposed deep model achieved an accuracy of 85.57%.

Kaynakça

  • Bingol, H. and B. Alatas. Rumor Detection in Social Media using machine learning methods. in 2019 1st International Informatics and Software Engineering Conference (UBMYK). 2019. IEEE.
  • Mani, I., et al., SUMMAC: a text summarization evaluation. Natural Language Engineering, 2002. 8(1): p. 43-68.
  • Bansal, N. and A. Singh. A review on opinionated sentiment analysis based upon machine learning approach. in 2016 International Conference on Inventive Computation Technologies (ICICT). 2016. IEEE.
  • Yadav, A. and D.K. Vishwakarma, Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 2020. 53(6): p. 4335-4385.
  • Elnagar, A., Y.S. Khalifa, and A. Einea, Hotel Arabic-reviews dataset construction for sentiment analysis applications, in Intelligent Natural Language Processing: Trends and Applications. 2018, Springer. p. 35-52.
  • Al Amrani, Y., M. Lazaar, and K.E. El Kadiri, Random forest and support vector machine based hybrid approach to sentiment analysis. Procedia Computer Science, 2018. 127: p. 511-520.
  • Haque, M.R., S.A. Lima, and S.Z. Mishu. Performance Analysis of Different Neural Networks for Sentiment Analysis on IMDb Movie Reviews. in 2019 3rd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE). 2019. IEEE.
  • Rao, G., et al., LSTM with sentence representations for document-level sentiment classification. Neurocomputing, 2018. 308: p. 49-57.
  • Islam, M.M. and N. Sultana, Comparative study on machine learning algorithms for sentiment classification. International Journal of Computer Applications, 2018. 182(21): p. 1-7.
  • Narayanan, V., I. Arora, and A. Bhatia. Fast and accurate sentiment classification using an enhanced Naive Bayes model. in International Conference on Intelligent Data Engineering and Automated Learning. 2013. Springer.
  • Huang, Y., et al. A topic BiLSTM model for sentiment classification. in Proceedings of the 2nd International Conference on Innovation in Artificial Intelligence. 2018.
  • Pang, B., L. Lee, and S. Vaithyanathan, Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070, 2002.
  • Matsumoto, S., H. Takamura, and M. Okumura. Sentiment classification using word sub-sequences and dependency sub-trees. in Pacific-Asia conference on knowledge discovery and data mining. 2005. Springer.
  • Tang, D. Sentiment-specific representation learning for document-level sentiment analysis. in Proceedings of the eighth ACM international conference on web search and data mining. 2015.
  • Liu, S.M. and J.-H. Chen, A multi-label classification based approach for sentiment classification. Expert Systems with Applications, 2015. 42(3): p. 1083-1093.
  • Maas, A., et al. Learning word vectors for sentiment analysis. in Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies. 2011.
  • A. Mullen, L., et al., Fast, consistent tokenization of natural language text. Journal of Open Source Software, 2018. 3(23): p. 655.
  • De Vries, E., M. Schoonvelde, and G. Schumacher, No longer lost in translation: Evidence that Google Translate works for comparative bag-of-words text applications. Political Analysis, 2018. 26(4): p. 417-430.
  • Sindagi, V.A. and V.M. Patel, A survey of recent advances in cnn-based single image crowd counting and density estimation. Pattern Recognition Letters, 2018. 107: p. 3-16.
  • Eroglu, Y., et al., Diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid model. Computer Methods and Programs in Biomedicine, 2021. 210: p. 106369.
  • Fuhl, W., et al. Training decision trees as replacement for convolution layers. in Proceedings of the AAAI Conference on Artificial Intelligence. 2020.
  • Gholamalinejad, H. and H. Khosravi, Vehicle classification using a real-time convolutional structure based on DWT pooling layer and SE blocks. Expert Systems with Applications, 2021. 183: p. 115420.
  • Wang, S.-H., et al., Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization. Neural Computing and Applications, 2020. 32(3): p. 665-680.
  • Ali, A.A.A. and S. Mallaiah, Intelligent handwritten recognition using hybrid CNN architectures based-SVM classifier with dropout. Journal of King Saud University-Computer and Information Sciences, 2021.
  • Lee, H., K. Bonin, and M. Guthold, Human mammary epithelial cells in a mature, stratified epithelial layer flatten and stiffen compared to single and confluent cells. Biochimica et Biophysica Acta (BBA)-General Subjects, 2021. 1865(6): p. 129891.
  • Lee, J.-S. and Y.-H. Byun, Instrumented cone penetrometer for dense layer characterization. Sensors, 2020. 20(20): p. 5782.
  • Zhang, M.-L. and Z.-H. Zhou, ML-KNN: A lazy learning approach to multi-label learning. Pattern recognition, 2007. 40(7): p. 2038-2048.
  • Rish, I. An empirical study of the naive Bayes classifier. in IJCAI 2001 workshop on empirical methods in artificial intelligence. 2001.
  • Pal, M., Random forest classifier for remote sensing classification. International journal of remote sensing, 2005. 26(1): p. 217-222.
  • Friedman, J.H., Stochastic gradient boosting. Computational statistics & data analysis, 2002. 38(4): p. 367-378.
  • Ke, G., et al., Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 2017. 30.
  • Klecka, W.R., G.R. Iversen, and W.R. Klecka, Discriminant analysis. Vol. 19. 1980: Sage.
  • Rätsch, G., T. Onoda, and K.-R. Müller, Soft margins for AdaBoost. Machine learning, 2001. 42(3): p. 287-320.
  • Chen, T. and C. Guestrin. Xgboost: A scalable tree boosting system. in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016.
  • Cengil, E., A. Çinar, and M. Yildirim. A Case Study: Cat-Dog Face Detector Based on YOLOv5. in 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). 2021. IEEE.
  • Yildirim, M. and A. Cinar, Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET. International Journal of Imaging Systems and Technology, 2022. 32(1): p. 155-162.

Film Yorumları Kullanılarak Önerilen Yapay Zekâ Tabanlı Yöntemle Duygu Analizinin Gerçekleştirilmesi

Yıl 2022, Cilt: 34 Sayı: 2, 751 - 760, 30.09.2022
https://doi.org/10.35234/fumbd.1138128

Öz

Gelişen teknoloji ile birlikte sosyal medya, forum siteleri ve bloklar yaygın bir şekilde kullanılmaya başlanmıştır. İnsanlar artık bu mecraları çok yaygın bir şekilde kullanmakta olup duygu ve düşüncelerini bu ortamlarda paylaşmaktadırlar. Bundan dolayı doğal dil işleme uygulamaları her geçen gün daha popüler bir konu haline gelmeye başlamıştır. Doğal dil işlemedeki en popüler konulardan birisi duygu analizidir. Duygu analizinde belirli kriterlere göre incelemeler yapılarak öznel bilgilerin çıkarılması sağlanmaktadır. Yapılan bu çalışmada duygu analizi yapmak için IMDB veri seti kullanılmıştır. IMDB veri seti, film yorumlarından oluşan bu konudaki en büyük veri setlerinden biridir. Bu veri seti kullanıcıların filmler hakkında ki yorumlarını içermektedir. Çalışmada, öncelikle veri önişleme adımı gerçekleştirilmiştir. Daha sonra hazırlanan veri seti klasik makine öğrenmesi sınıflandırıcılarında ve önerilen Evrişimsel Sinir Ağı ( ESA) tabanlı modelde sınıflandırılmıştır. Önerilen ESA tabanlı model IMDB veri setindeki metinleri analiz etme işleminde klasik makine öğrenmesi sınıflandırıcılarından daha başarılı olmuştur ve önerilen derin model %85.57 oranında bir doğruluk değeri elde etmiştir.

Kaynakça

  • Bingol, H. and B. Alatas. Rumor Detection in Social Media using machine learning methods. in 2019 1st International Informatics and Software Engineering Conference (UBMYK). 2019. IEEE.
  • Mani, I., et al., SUMMAC: a text summarization evaluation. Natural Language Engineering, 2002. 8(1): p. 43-68.
  • Bansal, N. and A. Singh. A review on opinionated sentiment analysis based upon machine learning approach. in 2016 International Conference on Inventive Computation Technologies (ICICT). 2016. IEEE.
  • Yadav, A. and D.K. Vishwakarma, Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 2020. 53(6): p. 4335-4385.
  • Elnagar, A., Y.S. Khalifa, and A. Einea, Hotel Arabic-reviews dataset construction for sentiment analysis applications, in Intelligent Natural Language Processing: Trends and Applications. 2018, Springer. p. 35-52.
  • Al Amrani, Y., M. Lazaar, and K.E. El Kadiri, Random forest and support vector machine based hybrid approach to sentiment analysis. Procedia Computer Science, 2018. 127: p. 511-520.
  • Haque, M.R., S.A. Lima, and S.Z. Mishu. Performance Analysis of Different Neural Networks for Sentiment Analysis on IMDb Movie Reviews. in 2019 3rd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE). 2019. IEEE.
  • Rao, G., et al., LSTM with sentence representations for document-level sentiment classification. Neurocomputing, 2018. 308: p. 49-57.
  • Islam, M.M. and N. Sultana, Comparative study on machine learning algorithms for sentiment classification. International Journal of Computer Applications, 2018. 182(21): p. 1-7.
  • Narayanan, V., I. Arora, and A. Bhatia. Fast and accurate sentiment classification using an enhanced Naive Bayes model. in International Conference on Intelligent Data Engineering and Automated Learning. 2013. Springer.
  • Huang, Y., et al. A topic BiLSTM model for sentiment classification. in Proceedings of the 2nd International Conference on Innovation in Artificial Intelligence. 2018.
  • Pang, B., L. Lee, and S. Vaithyanathan, Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070, 2002.
  • Matsumoto, S., H. Takamura, and M. Okumura. Sentiment classification using word sub-sequences and dependency sub-trees. in Pacific-Asia conference on knowledge discovery and data mining. 2005. Springer.
  • Tang, D. Sentiment-specific representation learning for document-level sentiment analysis. in Proceedings of the eighth ACM international conference on web search and data mining. 2015.
  • Liu, S.M. and J.-H. Chen, A multi-label classification based approach for sentiment classification. Expert Systems with Applications, 2015. 42(3): p. 1083-1093.
  • Maas, A., et al. Learning word vectors for sentiment analysis. in Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies. 2011.
  • A. Mullen, L., et al., Fast, consistent tokenization of natural language text. Journal of Open Source Software, 2018. 3(23): p. 655.
  • De Vries, E., M. Schoonvelde, and G. Schumacher, No longer lost in translation: Evidence that Google Translate works for comparative bag-of-words text applications. Political Analysis, 2018. 26(4): p. 417-430.
  • Sindagi, V.A. and V.M. Patel, A survey of recent advances in cnn-based single image crowd counting and density estimation. Pattern Recognition Letters, 2018. 107: p. 3-16.
  • Eroglu, Y., et al., Diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid model. Computer Methods and Programs in Biomedicine, 2021. 210: p. 106369.
  • Fuhl, W., et al. Training decision trees as replacement for convolution layers. in Proceedings of the AAAI Conference on Artificial Intelligence. 2020.
  • Gholamalinejad, H. and H. Khosravi, Vehicle classification using a real-time convolutional structure based on DWT pooling layer and SE blocks. Expert Systems with Applications, 2021. 183: p. 115420.
  • Wang, S.-H., et al., Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization. Neural Computing and Applications, 2020. 32(3): p. 665-680.
  • Ali, A.A.A. and S. Mallaiah, Intelligent handwritten recognition using hybrid CNN architectures based-SVM classifier with dropout. Journal of King Saud University-Computer and Information Sciences, 2021.
  • Lee, H., K. Bonin, and M. Guthold, Human mammary epithelial cells in a mature, stratified epithelial layer flatten and stiffen compared to single and confluent cells. Biochimica et Biophysica Acta (BBA)-General Subjects, 2021. 1865(6): p. 129891.
  • Lee, J.-S. and Y.-H. Byun, Instrumented cone penetrometer for dense layer characterization. Sensors, 2020. 20(20): p. 5782.
  • Zhang, M.-L. and Z.-H. Zhou, ML-KNN: A lazy learning approach to multi-label learning. Pattern recognition, 2007. 40(7): p. 2038-2048.
  • Rish, I. An empirical study of the naive Bayes classifier. in IJCAI 2001 workshop on empirical methods in artificial intelligence. 2001.
  • Pal, M., Random forest classifier for remote sensing classification. International journal of remote sensing, 2005. 26(1): p. 217-222.
  • Friedman, J.H., Stochastic gradient boosting. Computational statistics & data analysis, 2002. 38(4): p. 367-378.
  • Ke, G., et al., Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 2017. 30.
  • Klecka, W.R., G.R. Iversen, and W.R. Klecka, Discriminant analysis. Vol. 19. 1980: Sage.
  • Rätsch, G., T. Onoda, and K.-R. Müller, Soft margins for AdaBoost. Machine learning, 2001. 42(3): p. 287-320.
  • Chen, T. and C. Guestrin. Xgboost: A scalable tree boosting system. in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016.
  • Cengil, E., A. Çinar, and M. Yildirim. A Case Study: Cat-Dog Face Detector Based on YOLOv5. in 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). 2021. IEEE.
  • Yildirim, M. and A. Cinar, Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET. International Journal of Imaging Systems and Technology, 2022. 32(1): p. 155-162.
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm MBD
Yazarlar

Muhammed Yıldırım 0000-0003-1866-4721

Yayımlanma Tarihi 30 Eylül 2022
Gönderilme Tarihi 30 Haziran 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 34 Sayı: 2

Kaynak Göster

APA Yıldırım, M. (2022). Film Yorumları Kullanılarak Önerilen Yapay Zekâ Tabanlı Yöntemle Duygu Analizinin Gerçekleştirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(2), 751-760. https://doi.org/10.35234/fumbd.1138128
AMA Yıldırım M. Film Yorumları Kullanılarak Önerilen Yapay Zekâ Tabanlı Yöntemle Duygu Analizinin Gerçekleştirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. Eylül 2022;34(2):751-760. doi:10.35234/fumbd.1138128
Chicago Yıldırım, Muhammed. “Film Yorumları Kullanılarak Önerilen Yapay Zekâ Tabanlı Yöntemle Duygu Analizinin Gerçekleştirilmesi”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34, sy. 2 (Eylül 2022): 751-60. https://doi.org/10.35234/fumbd.1138128.
EndNote Yıldırım M (01 Eylül 2022) Film Yorumları Kullanılarak Önerilen Yapay Zekâ Tabanlı Yöntemle Duygu Analizinin Gerçekleştirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34 2 751–760.
IEEE M. Yıldırım, “Film Yorumları Kullanılarak Önerilen Yapay Zekâ Tabanlı Yöntemle Duygu Analizinin Gerçekleştirilmesi”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 34, sy. 2, ss. 751–760, 2022, doi: 10.35234/fumbd.1138128.
ISNAD Yıldırım, Muhammed. “Film Yorumları Kullanılarak Önerilen Yapay Zekâ Tabanlı Yöntemle Duygu Analizinin Gerçekleştirilmesi”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34/2 (Eylül 2022), 751-760. https://doi.org/10.35234/fumbd.1138128.
JAMA Yıldırım M. Film Yorumları Kullanılarak Önerilen Yapay Zekâ Tabanlı Yöntemle Duygu Analizinin Gerçekleştirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2022;34:751–760.
MLA Yıldırım, Muhammed. “Film Yorumları Kullanılarak Önerilen Yapay Zekâ Tabanlı Yöntemle Duygu Analizinin Gerçekleştirilmesi”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 34, sy. 2, 2022, ss. 751-60, doi:10.35234/fumbd.1138128.
Vancouver Yıldırım M. Film Yorumları Kullanılarak Önerilen Yapay Zekâ Tabanlı Yöntemle Duygu Analizinin Gerçekleştirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2022;34(2):751-60.