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CLASSIFICATION OF CUSTOMER SENTIMENTS BASED ON ONLINE REVIEWS: COMPARATIVE ANALYSIS OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS Skip to content Yıl 2024, Cilt: 27 Sayı: 3, 779 – 799, 03.09.2024
Vahid Sinap
https://doi.org/10.17780/ksujes.1420530
Öz E-commerce’s transformation of consumer behavior has increased the importance of understanding customer emotions, especially in the transition from traditional retail models to online platforms. The proliferation of online shopping has fundamentally changed not only shopping habits but also consumer interactions and purchase decisions. This research aims to compare and analyze the performance of various text mining and machine learning algorithms in the context of sentiment analysis and online review data. For this purpose, analyses were performed with a total of five supervised classification algorithms including Logistic Regression, Naive Bayes, Support Vector Machine, Random Forest, AdaBoost, and a deep learning model, CNN Model. The dataset used in the study includes customer reviews obtained from a women’s clothing e-commerce platform. The missing data were completed by pre-processing the dataset. Count Vectorizer and TF-IDF vectorization were performed to transform the textual data. In addition, various text preprocessing steps were applied. According to the findings obtained from the research, AdaBoost and Naive Bayes algorithms were the most effective algorithms in terms of classifying customer sentiments. No significant difference was detected in terms of the vectorization method used. Although the CNN Model showed high performance, the generalizability of the model was considered low because overfitting was detected during the training of the model.
Anahtar Kelimeler Natural language processing, sentiment analysis, text mining, machine learning, deep learning
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Dey, S., Wasif, S., Tonmoy, D. S., Sultana, S., Sarkar, J., & Dey, M. (2020, February). A comparative study of support vector machine and Naive Bayes classifier for sentiment analysis on Amazon product reviews. In 2020 International Conference on Contemporary Computing and Applications (IC3A) (pp. 217-220). IEEE. Dogru, N., & Subasi, A. (2018, February). Traffic accident detection using random forest classifier. In 2018 15th learning and technology conference (L&T) (pp. 40-45). IEEE. Feng, X., Liang, Y., Shi, X., Xu, D., Wang, X., & Guan, R. (2017). Overfitting reduction of text classification based on AdaBELM. Entropy, 19(7), 330. Georgescu, I., & Kinnunen, J. (2020). Consumer recommendation dynamics in online retail business under logistic regression and naïve Bayes analyses. In Proceedings of the International Conference on Applied Statistics (Vol. 2, No. 1, pp. 120-128). Guo, J. (2022). Deep learning approach to text analysis for human emotion detection from big data. 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Hu, W., Gong, Z., & Guo, J. (2010, November). Mining product features from online reviews. In 2010 IEEE 7th International Conference on E-Business Engineering (pp. 24-29). IEEE. Inácio, M., & Oliveira, H. G. (2024, March). Exploring multimodal models for humor recognition in Portuguese. In Proceedings of the 16th International Conference on Computational Processing of Portuguese (pp. 568-574). Jararweh, Y., Al-Ayyoub, M., Fakirah, M., Alawneh, L., & Gupta, B. B. (2019). Improving the performance of the needleman-wunsch algorithm using parallelization and vectorization techniques. Multimedia Tools and Applications, 78, 3961-3977. Kamal, M., & Himel, A. S. (2023). Redefining Modern Marketing: An analysis of AI and NLP's influence on consumer engagement, strategy, and beyond. Eigenpub Review of Science and Technology, 7(1), 203-223. Kapoor, S., & Banerjee, S. (2021). On the relationship between brand scandal and consumer attitudes: A literature review and research agenda. International Journal of Consumer Studies, 45(5), 1047-1078. Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: A survey. Information, 10(4), 150. Li, C., Zhang, Z., Lee, W. S., & Lee, G. H. (2018). Convolutional sequence to sequence model for human dynamics. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5226-5234). Li, H., Bruce, X. B., Li, G., & Gao, H. (2023). Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, 104707. Li, X., Wang, L., & Sung, E. (2005, July). A study of AdaBoost with SVM based weak learners. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. (Vol. 1, pp. 196-201). IEEE. Li, X., Wang, L., & Sung, E. (2008). AdaBoost with SVM-based component classifiers. Engineering Applications of Artificial Intelligence, 21(5), 785-795. Lian, H., Lu, C., Li, S., Zhao, Y., Tang, C., & Zong, Y. (2023). A survey of deep learning-based multimodal emotion recognition: speech, text, and face. Entropy, 25(10), 1440. Maronikolakis, A., & Schütze, H. (2021, April). Multidomain pretrained language models for green NLP. In Proceedings of the Second Workshop on Domain Adaptation for NLP (pp. 1-8). Mariani, M., & Borghi, M. (2021). Are environmental-related online reviews more helpful? A big data analytics approach. International Journal of Contemporary Hospitality Management, 33(6), 2065-2090. Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep learning–based text classification: a comprehensive review. ACM Computing Surveys (CSUR), 54(3), 1-40. Mohammad, S. M. (2016). Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In Emotion measurement (pp. 201-237). Woodhead Publishing. Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1-21. Obiedat, R., Qaddoura, R., Ala’M, A. Z., Al-Qaisi, L., Harfoushi, O., Alrefai, M. A., & Faris, H. (2022). Sentiment analysis of customers’ reviews using a hybrid evolutionary svm-based approach in an imbalanced data distribution. IEEE Access, 10, 22260-22273. Patel, A., Oza, P., & Agrawal, S. (2023). Sentiment analysis of customer feedback and reviews for airline services using language representation model. Procedia Computer Science, 218, 2459-2467. Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. In Machine learning (pp. 101-121). Academic Press. Pradhan, V. M., Vala, J., & Balani, P. (2016). A survey on sentiment analysis algorithms for opinion mining. International Journal of Computer Applications, 133(9), 7-11. Qaiser, S., & Ali, R. (2018). Text mining: use of TF-IDF to examine the relevance of words to documents. International Journal of Computer Applications, 181(1), 25-29. Racherla, P., & Friske, W. (2012). Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11(6), 548-559. Rain, C. (2013). Sentiment analysis in Amazon reviews using probabilistic machine learning. Swarthmore College, 42. Ramadhan, F. A., Ruslan, R. R. P., & Zahra, A. (2023). Sentiment analysis of e-commerce product reviews for content interaction using machine learning. Cakrawala Repositori IMWI, 6(1), 207-220. Robertson, S. (2004). Understanding inverse document frequency: on theoretical arguments for IDF. Journal of Documentation, 60(5), 503-520. Rosário, A., & Raimundo, R. (2021). Consumer marketing strategy and e-commerce in the last decade: a literature review. Journal of Theoretical and Applied Electronic Commerce Research, 16(7), 3003-3024. Rygielski, C., Wang, J. C., & Yen, D. C. (2002). Data mining techniques for customer relationship management. Technology in Society, 24(4), 483-502. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. Shanthi, R., & Desti, K. (2015). Consumers' perception on online shopping. Journal of Marketing and Consumer Research, 13, 14-21. Singh, J. P., Irani, S., Rana, N. P., Dwivedi, Y. K., Saumya, S., & Roy, P. K. (2017). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70, 346-355. Sylvester, E. V., Bentzen, P., Bradbury, I. R., Clément, M., Pearce, J., Horne, J., & Beiko, R. G. (2018). Applications of random forest feature selection for fine‐scale genetic population assignment. Evolutionary Applications, 11(2), 153-165. Tran, D. D., Nguyen, T. T. S., & Dao, T. H. C. (2022). Sentiment analysis of movie reviews using machine learning techniques. In Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 1 (pp. 361-369). Springer Singapore. Turki, T., & Roy, S. S. (2022). Novel hate speech detection using word cloud visualization and ensemble learning coupled with count vectorizer. Applied Sciences, 12(13), 6611. Vijayarani, S., & Janani, R. (2016). Text mining: open source tokenization tools-an analysis. Advanced Computational Intelligence: An International Journal (ACII), 3(1), 37-47. Wani, T. A., & Ali, S. W. (2015). Innovation diffusion theory. Journal of General Management Research, 3(2), 101-118. Weiss, S. M., Indurkhya, N., Zhang, T., & Damerau, F. (2010). Text mining: predictive methods for analyzing unstructured information. Springer Science & Business Media. Wyner, A. J., Olson, M., Bleich, J., & Mease, D. (2017). Explaining the success of adaboost and random forests as interpolating classifiers. The Journal of Machine Learning Research, 18(1), 1558-1590. Xia, R., Zong, C., & Li, S. (2011). Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences, 181(6), 1138-1152. Xiong, H., Pandey, G., Steinbach, M., & Kumar, V. (2006). Enhancing data analysis with noise removal. IEEE Transactions on Knowledge and Data Engineering, 18(3), 304-319. Zhang, F., Fleyeh, H., Wang, X., & Lu, M. (2019). Construction site accident analysis using text mining and natural language processing techniques. Automation in Construction, 99, 238-248. Zhang, J., Lu, X., & Liu, D. (2021). Deriving customer preferences for hotels based on aspect-level sentiment analysis of online reviews. Electronic Commerce Research and Applications, 49, 101094. Zhao, H., Liu, Z., Yao, X., & Yang, Q. (2021). A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach. Information Processing & Management, 58(5), 102656. Zhou, J., & Troyanskaya, O. G. (2015). Predicting effects of noncoding variants with deep learning–based sequence model. Nature Methods, 12(10), 931-934. Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350-361. Zhuang, M., Cui, G., & Peng, L. (2018). Manufactured opinions: The effect of manipulating online product reviews. Journal of Business Research, 87, 24-35.
ÇEVRİMİÇİ DEĞERLENDİRMELER ÜZERİNDEN MÜŞTERİ DUYGULARININ SINIFLANDIRILMASI: MAKİNE ÖĞRENMESİ VE DERİN ÖĞRENME ALGORİTMALARININ KARŞILAŞTIRMALI Yıl 2024, Cilt: 27 Sayı: 3, 779 – 799, 03.09.2024
Vahid Sinap
https://doi.org/10.17780/ksujes.1420530
Öz Geleneksel perakende modellerinden çevrimiçi platformlara geçişte e-ticaretin tüketici davranışlarını dönüştürücü etkisi müşteri duygularını anlamanın önemini artırmıştır. Bu araştırma, çeşitli metin madenciliği ve makine öğrenmesi algoritmalarının duygu analizi ve çevrimiçi değerlendirme verileri bağlamında performanslarını karşılaştırmayı amaçlamaktadır. Bu amaç doğrultusunda Lojistik Regresyon, Naive Bayes, Destek Vektör Makinesi, Rastgele Orman ve AdaBoost olmak üzere toplam beş denetimli sınıflandırma algoritması ve bir derin öğrenme modeli olan CNN Model ile analizler gerçekleştirilmiştir. Çalışmada kullanılan veri seti, bir kadın giyim e-ticaret platformundan elde edilen müşteri değerlendirmelerini içermektedir. Veri setinde ön işlemeler gerçekleştirilerek eksik veriler tamamlanmıştır. Count Vectorizer ve TF-IDF vektörizasyonları yapılarak metinsel verilerin dönüşümü sağlanmıştır. Bunlara ek olarak çeşitli metin ön işleme adımları uygulanmıştır. Araştırmadan elde edilen bulgulara göre müşteri duygularını sınıflandırma bağlamında en etkili algoritmalar AdaBoost ve Naive Bayes algoritmaları olmuştur. Kullanılan vektörizasyon yöntemi açısından önemli bir farklılık tespit edilmemiştir. CNN Model yüksek performans gösterse de modelin eğitimi sırasında aşırı öğrenme tespit edildiği için modelin genellenebilirliği düşük kabul edilmiştir.
Anahtar Kelimeler Doğal dil işleme, duygu analizi, metin madenciliği, makine öğrenmesi, derin öğrenme
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International Journal of Consumer Studies, 45(5), 1047-1078. Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: A survey. Information, 10(4), 150. Li, C., Zhang, Z., Lee, W. S., & Lee, G. H. (2018). Convolutional sequence to sequence model for human dynamics. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5226-5234). Li, H., Bruce, X. B., Li, G., & Gao, H. (2023). Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, 104707. Li, X., Wang, L., & Sung, E. (2005, July). A study of AdaBoost with SVM based weak learners. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. (Vol. 1, pp. 196-201). IEEE. Li, X., Wang, L., & Sung, E. (2008). AdaBoost with SVM-based component classifiers. Engineering Applications of Artificial Intelligence, 21(5), 785-795. Lian, H., Lu, C., Li, S., Zhao, Y., Tang, C., & Zong, Y. (2023). A survey of deep learning-based multimodal emotion recognition: speech, text, and face. Entropy, 25(10), 1440. Maronikolakis, A., & Schütze, H. (2021, April). Multidomain pretrained language models for green NLP. In Proceedings of the Second Workshop on Domain Adaptation for NLP (pp. 1-8). Mariani, M., & Borghi, M. (2021). Are environmental-related online reviews more helpful? A big data analytics approach. International Journal of Contemporary Hospitality Management, 33(6), 2065-2090. Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep learning–based text classification: a comprehensive review. ACM Computing Surveys (CSUR), 54(3), 1-40. Mohammad, S. M. (2016). Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In Emotion measurement (pp. 201-237). Woodhead Publishing. Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1-21. Obiedat, R., Qaddoura, R., Ala’M, A. Z., Al-Qaisi, L., Harfoushi, O., Alrefai, M. A., & Faris, H. (2022). Sentiment analysis of customers’ reviews using a hybrid evolutionary svm-based approach in an imbalanced data distribution. IEEE Access, 10, 22260-22273. Patel, A., Oza, P., & Agrawal, S. (2023). Sentiment analysis of customer feedback and reviews for airline services using language representation model. Procedia Computer Science, 218, 2459-2467. Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. In Machine learning (pp. 101-121). Academic Press. Pradhan, V. M., Vala, J., & Balani, P. (2016). A survey on sentiment analysis algorithms for opinion mining. International Journal of Computer Applications, 133(9), 7-11. Qaiser, S., & Ali, R. (2018). Text mining: use of TF-IDF to examine the relevance of words to documents. International Journal of Computer Applications, 181(1), 25-29. Racherla, P., & Friske, W. (2012). Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11(6), 548-559. Rain, C. (2013). Sentiment analysis in Amazon reviews using probabilistic machine learning. Swarthmore College, 42. Ramadhan, F. A., Ruslan, R. R. P., & Zahra, A. (2023). Sentiment analysis of e-commerce product reviews for content interaction using machine learning. Cakrawala Repositori IMWI, 6(1), 207-220. Robertson, S. (2004). Understanding inverse document frequency: on theoretical arguments for IDF. Journal of Documentation, 60(5), 503-520. Rosário, A., & Raimundo, R. (2021). Consumer marketing strategy and e-commerce in the last decade: a literature review. Journal of Theoretical and Applied Electronic Commerce Research, 16(7), 3003-3024. Rygielski, C., Wang, J. C., & Yen, D. C. (2002). Data mining techniques for customer relationship management. Technology in Society, 24(4), 483-502. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. Shanthi, R., & Desti, K. (2015). Consumers' perception on online shopping. Journal of Marketing and Consumer Research, 13, 14-21. Singh, J. P., Irani, S., Rana, N. P., Dwivedi, Y. K., Saumya, S., & Roy, P. K. (2017). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70, 346-355. Sylvester, E. V., Bentzen, P., Bradbury, I. R., Clément, M., Pearce, J., Horne, J., & Beiko, R. G. (2018). Applications of random forest feature selection for fine‐scale genetic population assignment. Evolutionary Applications, 11(2), 153-165. Tran, D. D., Nguyen, T. T. S., & Dao, T. H. C. (2022). Sentiment analysis of movie reviews using machine learning techniques. In Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 1 (pp. 361-369). Springer Singapore. Turki, T., & Roy, S. S. (2022). Novel hate speech detection using word cloud visualization and ensemble learning coupled with count vectorizer. Applied Sciences, 12(13), 6611. Vijayarani, S., & Janani, R. (2016). Text mining: open source tokenization tools-an analysis. Advanced Computational Intelligence: An International Journal (ACII), 3(1), 37-47. Wani, T. A., & Ali, S. W. (2015). Innovation diffusion theory. Journal of General Management Research, 3(2), 101-118. Weiss, S. M., Indurkhya, N., Zhang, T., & Damerau, F. (2010). Text mining: predictive methods for analyzing unstructured information. Springer Science & Business Media. Wyner, A. J., Olson, M., Bleich, J., & Mease, D. (2017). Explaining the success of adaboost and random forests as interpolating classifiers. The Journal of Machine Learning Research, 18(1), 1558-1590. Xia, R., Zong, C., & Li, S. (2011). Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences, 181(6), 1138-1152. Xiong, H., Pandey, G., Steinbach, M., & Kumar, V. (2006). Enhancing data analysis with noise removal. IEEE Transactions on Knowledge and Data Engineering, 18(3), 304-319. Zhang, F., Fleyeh, H., Wang, X., & Lu, M. (2019). Construction site accident analysis using text mining and natural language processing techniques. Automation in Construction, 99, 238-248. Zhang, J., Lu, X., & Liu, D. (2021). Deriving customer preferences for hotels based on aspect-level sentiment analysis of online reviews. Electronic Commerce Research and Applications, 49, 101094. Zhao, H., Liu, Z., Yao, X., & Yang, Q. (2021). A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach. Information Processing & Management, 58(5), 102656. Zhou, J., & Troyanskaya, O. G. (2015). Predicting effects of noncoding variants with deep learning–based sequence model. Nature Methods, 12(10), 931-934. Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350-361. Zhuang, M., Cui, G., & Peng, L. (2018). Manufactured opinions: The effect of manipulating online product reviews. Journal of Business Research, 87, 24-35. Toplam 76 adet kaynakça vardır.
Ayrıntılar Birincil Dil İngilizce Konular Veri Madenciliği ve Bilgi Keşfi, Doğal Dil İşleme Bölüm Bilgisayar Mühendisliği Yazarlar Vahid Sinap Ufuk Üniversitesi 0000-0002-8734-9509 Türkiye
Yayımlanma Tarihi 3 Eylül 2024 Gönderilme Tarihi 16 Ocak 2024 Kabul Tarihi 31 Temmuz 2024 Yayımlandığı Sayı Yıl 2024Cilt: 27 Sayı: 3
Kaynak Göster APA Sinap, V. (2024). CLASSIFICATION OF CUSTOMER SENTIMENTS BASED ON ONLINE REVIEWS: COMPARATIVE ANALYSIS OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 779-799. https://doi.org/10.17780/ksujes.1420530
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