Sentiment Analysis Of E-Commerce Product Reviews For Content Interaction Using Machine Learning
Main Article Content
With the growth of various e-commerce applications, it has become increasingly important to understand consumer spending patterns and provide products that best respond to their needs. One way of doing this is by utilizing the data available in consumer product reviews to improve the level of content interaction. I
n order to tap into this data, companies can utilize a process called sentiment analysis to identify consumers’ sentiments or reactions towards certain products. This study proposes to compare different methods of sentiment analysis focused on one specific product, utilizing a machine learning approach with Naïve Bayes and Support Vector Machine classifiers with the aim of finding which method produces the best results in terms of accuracy, precision, recall and F-measure. A specific focus has been made to analyze the consumer reviews of wireless earphone products from the Indonesian e-commerce company Tokopedia. The results of this study show that utilizing the Naïve Bayes classifier, enhanced with hyperparameter tuning produced the best results in terms of accuracy, recall, and F-measure with a value of 77.03%, 73.03%, and 74.41% respectively. Whereas the best precision was obtained by utilizing an SVM classifier enhanced with hyperparameter tuning, producing a value of 76.05%.
Keywords:
Sentiment Analysis, E-Commerce, Content Interaction, Machine Learning Approach, Confusion Matrix
Bayhaqy, A., Sfenrianto, S., Nainggolan, K., & Kaburuan, E. R. (2018). Sentiment analysis about E-commerce from tweets using decision tree, K-nearest neighbor, and naïve bayes. 2018 International Conference on Orange Technologies (ICOT), 1–6. https://doi.org/10.1109/ICOT.2018.8705796
Berrar, D. (2019). Cross-Validation. https://www.researchgate.net/profile/Daniel-Berrar/publication/324701535_Cross-Validation/links/5cb4209c92851c8d22ec4349/Cross-Validation.pdf
Handayani, Y., & Hakim, A. R. (2020). Sentiment Analysis of Bank BNI User Comments Using the Support Vector Machine Method. 2020 International Seminar on Application for Technology of Information and Communication (ISemantic), 202–207. https://ieeexplore.ieee.org/abstract/document/9234230/
Hariawansah, M. (2018). Analisis Sentimen E-Commerce Dengan Menggunakan Metode Naive Bayes. In Department of Computer Science, Binus University Graduate Program.
Jurek, A., Mulvenna, M. D., & Bi, Y. (2015). Improved lexicon-based sentiment analysis for social media analytics. Security Informatics, 4(1), 1–13. https://doi.org/10.1186/s13388-015-0024
Leung, K. M. (2007). Naive bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering, 2007, 123–156. https://cse.engineering.nyu.edu/~mleung/FRE7851/f07/naiveBayesianClassifier.pdf
Marong, M., Batcha, N. K., & Mafas, R. (2020). Sentiment Analysis in E-Commerce: A Review on The Techniques and Algorithms. Journal of Applied Technology and Innovation (e-ISSN: 2600-7304), 4(1), 6. https://jati.sites.apiit.edu.my/files/2020/01/Sentiment-Analysis-in-E-Commerce-A-Review-on-The-Techniques-and-Algorithms.pdf
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
Noor, A., & Islam, M. (2019). Sentiment Analysis for Women’s E-commerce Reviews using Machine Learning Algorithms. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1–6. https://doi.org/10.1109/ICCCNT45670.2019.8944436
Pannakkong, W., Thiwa-Anont, K., Singthong, K., Parthanadee, P., & Buddhakulsomsiri, J. (2022). Hyperparameter tuning of machine learning algorithms using response surface methodology: a case study of ANN, SVM, and DBN. Mathematical Problems in Engineering, 2022, 1–17. https://doi.org/10.1155/2022/8513719
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. https://doi.org/10.5120/ijca2018917395
Romadhon, R. (2021). Kenali 5 Model Bisnis eCommerce (B2B, B2C, C2C, C2B, B2G) Sebelum Membangun Sistem Untuk Bisnis. https://www.softwareseni.co.id/blog/5-model-bisnis-ecommerce-b2b-b2c-c2c-c2b-b2g
S. Visa, B. Ramsay, A. Ralescu, and E. van der K. (2011). Confusion Matrix-based Feature Selection. Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference 2011.
Sari, P. K., Alamsyah, A., & Wibowo, S. (2018). Measuring e-Commerce service quality from online customer review using sentiment analysis. Journal of Physics: Conference Series, 971(1), 12053. https://doi.org/10.1088/1742-6596/971/1/012053
Sharma, M. (2020). Sentiment Analysis: An Introduction to Naive Bayes Algorithm. https://towardsdatascience.com/sentiment-analysis-introduction-to-naive-bayes-algorithm-96831d77ac91
Simon, A. R., & Shaffer, S. L. (2001). Data warehousing and business intelligence for e-commerce. Elsevier. https://books.google.co.id/books?hl=en&lr=&id=-lqQMHnxPU4C&oi=fnd&pg=PP1&dq=Foundations:+Concepts+and+Business+Models”,+in+Data+Warehousing+and+Business+Intelligence+for+E-Commerce,+1st+ed&ots=qI4elxGwka&sig=7nmTxBuyi-JE9lno8pv8Qo3nVt4&redir_esc=y#v=onepa
Suresh, A., & Bharathi, C. R. (2016). Sentiment classification using decision tree based feature selection. Ijcta, 9(36), 419–425. https://www.researchgate.net/profile/Annamalai-Suresh/publication/312068124_Sentiment_Classifi_cation_using_Decision_Tree_Based_Feature_Selection/links/586dd8dc08aebf17d3a7327e/Sentiment-Classifi-cation-using-Decision-Tree-Based-Feature-Selection.pdf
T. Willianto, Supryadi, and A. W. (2020). Sentiment Analysis on E-Commerce Product using Machine Learning and Combination of TF-IDF and Backward Elimination. International Journal of Recent Technology and Engineering (IJRTE),. https://doi.org/10.35940/ijrte.F7889.038620
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307. https://doi.org/10.1162/COLI_a_00049
Tokopedia. (2021). Kisah Kami. https://www.tokopedia.com/about/our-story/
Vanaja, S., & Belwal, M. (2018). Aspect-level sentiment analysis on e-commerce data. 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 1275–1279. https://doi.org/10.1109/ICIRCA.2018.8597286
Wu, J., Chen, X.-Y., Zhang, H., Xiong, L.-D., Lei, H., & Deng, S.-H. (2019). Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17(1), 26–40. https://doi.org/10.11989/JEST.1674-862X.80904120
Zainuddin, N., & Selamat, A. (2014). Sentiment analysis using support vector machine. 2014 International Conference on Computer, Communications, and Control Technology (I4CT), 333–337. Sentiment Analysis Using Support Vector Machine. Presented at International Conference on Computer, Communication, and Control Technology.
Berrar, D. (2019). Cross-Validation. https://www.researchgate.net/profile/Daniel-Berrar/publication/324701535_Cross-Validation/links/5cb4209c92851c8d22ec4349/Cross-Validation.pdf
Handayani, Y., & Hakim, A. R. (2020). Sentiment Analysis of Bank BNI User Comments Using the Support Vector Machine Method. 2020 International Seminar on Application for Technology of Information and Communication (ISemantic), 202–207. https://ieeexplore.ieee.org/abstract/document/9234230/
Hariawansah, M. (2018). Analisis Sentimen E-Commerce Dengan Menggunakan Metode Naive Bayes. In Department of Computer Science, Binus University Graduate Program.
Jurek, A., Mulvenna, M. D., & Bi, Y. (2015). Improved lexicon-based sentiment analysis for social media analytics. Security Informatics, 4(1), 1–13. https://doi.org/10.1186/s13388-015-0024
Leung, K. M. (2007). Naive bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering, 2007, 123–156. https://cse.engineering.nyu.edu/~mleung/FRE7851/f07/naiveBayesianClassifier.pdf
Marong, M., Batcha, N. K., & Mafas, R. (2020). Sentiment Analysis in E-Commerce: A Review on The Techniques and Algorithms. Journal of Applied Technology and Innovation (e-ISSN: 2600-7304), 4(1), 6. https://jati.sites.apiit.edu.my/files/2020/01/Sentiment-Analysis-in-E-Commerce-A-Review-on-The-Techniques-and-Algorithms.pdf
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
Noor, A., & Islam, M. (2019). Sentiment Analysis for Women’s E-commerce Reviews using Machine Learning Algorithms. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1–6. https://doi.org/10.1109/ICCCNT45670.2019.8944436
Pannakkong, W., Thiwa-Anont, K., Singthong, K., Parthanadee, P., & Buddhakulsomsiri, J. (2022). Hyperparameter tuning of machine learning algorithms using response surface methodology: a case study of ANN, SVM, and DBN. Mathematical Problems in Engineering, 2022, 1–17. https://doi.org/10.1155/2022/8513719
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. https://doi.org/10.5120/ijca2018917395
Romadhon, R. (2021). Kenali 5 Model Bisnis eCommerce (B2B, B2C, C2C, C2B, B2G) Sebelum Membangun Sistem Untuk Bisnis. https://www.softwareseni.co.id/blog/5-model-bisnis-ecommerce-b2b-b2c-c2c-c2b-b2g
S. Visa, B. Ramsay, A. Ralescu, and E. van der K. (2011). Confusion Matrix-based Feature Selection. Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference 2011.
Sari, P. K., Alamsyah, A., & Wibowo, S. (2018). Measuring e-Commerce service quality from online customer review using sentiment analysis. Journal of Physics: Conference Series, 971(1), 12053. https://doi.org/10.1088/1742-6596/971/1/012053
Sharma, M. (2020). Sentiment Analysis: An Introduction to Naive Bayes Algorithm. https://towardsdatascience.com/sentiment-analysis-introduction-to-naive-bayes-algorithm-96831d77ac91
Simon, A. R., & Shaffer, S. L. (2001). Data warehousing and business intelligence for e-commerce. Elsevier. https://books.google.co.id/books?hl=en&lr=&id=-lqQMHnxPU4C&oi=fnd&pg=PP1&dq=Foundations:+Concepts+and+Business+Models”,+in+Data+Warehousing+and+Business+Intelligence+for+E-Commerce,+1st+ed&ots=qI4elxGwka&sig=7nmTxBuyi-JE9lno8pv8Qo3nVt4&redir_esc=y#v=onepa
Suresh, A., & Bharathi, C. R. (2016). Sentiment classification using decision tree based feature selection. Ijcta, 9(36), 419–425. https://www.researchgate.net/profile/Annamalai-Suresh/publication/312068124_Sentiment_Classifi_cation_using_Decision_Tree_Based_Feature_Selection/links/586dd8dc08aebf17d3a7327e/Sentiment-Classifi-cation-using-Decision-Tree-Based-Feature-Selection.pdf
T. Willianto, Supryadi, and A. W. (2020). Sentiment Analysis on E-Commerce Product using Machine Learning and Combination of TF-IDF and Backward Elimination. International Journal of Recent Technology and Engineering (IJRTE),. https://doi.org/10.35940/ijrte.F7889.038620
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307. https://doi.org/10.1162/COLI_a_00049
Tokopedia. (2021). Kisah Kami. https://www.tokopedia.com/about/our-story/
Vanaja, S., & Belwal, M. (2018). Aspect-level sentiment analysis on e-commerce data. 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 1275–1279. https://doi.org/10.1109/ICIRCA.2018.8597286
Wu, J., Chen, X.-Y., Zhang, H., Xiong, L.-D., Lei, H., & Deng, S.-H. (2019). Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17(1), 26–40. https://doi.org/10.11989/JEST.1674-862X.80904120
Zainuddin, N., & Selamat, A. (2014). Sentiment analysis using support vector machine. 2014 International Conference on Computer, Communications, and Control Technology (I4CT), 333–337. Sentiment Analysis Using Support Vector Machine. Presented at International Conference on Computer, Communication, and Control Technology.
