Sentiment analysis for Arabic call center notes using machine learning techniques
Abstract
Call centers handle thousands of incoming calls daily, encompassing a diverse array of categories including product inquiries, complaints, and more. Within these conversations, customers articulate their opinions and interests in the products and services offered. Effectively categorizing and analyzing these calls holds immense importance for organizations, offering a window into their strengths, weaknesses, and gauging customer satisfaction and needs. This paper introduces an innovative approach to extract customer sentiments through an advanced sentiment analysis technique. Leveraging two distinct yet synergistic algorithms—Support Vector Machine (SVM) and Neural Networks (NNs)—on the Kaggle machine-learning platform, our method discerns the polarity of each note, classifying them as positive, negative, or neutral. To enhance the quality of our analysis, we employed Natural Language Processing (NLP) and a range of preprocessing tools, including tokenization. The dataset comprises three thousand notes from various telecommunication companies, authored during real call center interactions. These notes form the basis of a specialized corpus, notable for being composed in the Jordanian dialect. Rigorous training and testing procedures were conducted using this corpus. The results are notable: our proposed algorithms displayed strong performance metrics. SVM yielded a commendable accuracy rate of 66%, while NNs excelled, boasting an impressive accuracy rate of 99.21%. These achievements are substantiated by comprehensive confusion matrices. In conclusion, our research provides a novel and robust framework for customer sentiment analysis in call centers, underpinned by the fusion of SVM and NNs. This technique promises valuable insights into customer feedback, facilitating informed decision-making for businesses seeking to enhance their services and products.
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1. Prabaningtyas NI, Surjandari I, Laoh E. Mining Customers Opinion on Services and Applications of Mobile Payment Companies in Indonesia Using Sentiment Analysis Approach. 2019 16th International Conference on Service Systems and Service Management (ICSSSM); July 2019. doi: 10.1109/icsssm.2019.8887643
2. Hartama D, Perdana Windarto A, Wanto A. The Application of Data Mining in Determining Patterns of Interest of High School Graduates. Journal of Physics: Conference Series. 2019, 1339(1): 012042. doi: 10.1088/1742-6596/1339/1/012042
3. Sadr H, Pedram MM, Teshnehlab M. A Robust Sentiment Analysis Method Based on Sequential Combination of Convolutional and Recursive Neural Networks. Neural Processing Letters. 2019, 50(3): 2745-2761. doi: 10.1007/s11063-019-10049-1
4. Yadav A, Vishwakarma DK. Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review. 2019, 53(6): 4335-4385. doi: 10.1007/s10462-019-09794-
5. Gamal D, Alfonse M, El-Horbaty ESM, Salem ABM. Twitter Benchmark Dataset for Arabic Sentiment Analysis. International Journal of Modern Education and Computer Science. 2019, 11(1): 33-38. doi: 10.5815/ijmecs.2019.01.04
6. Wang M, Cai Q, Wang L, et al. Chinese news text classification based on attention-based CNN-BiLSTM. In: Liu Z, Udupa JK, Sang N, Wang Y (editors). MIPPR 2019: Pattern Recognition and Computer Vision. 2020. doi: 10.1117/12.253813
7. Majumder S, Aich A, Das S. Sentiment Analysis of People During Lockdown Period of COVID-19 Using SVM and Logistic Regression Analysis. SSRN Electronic Journal. 2021. doi: 10.2139/ssrn.3801039
8. Abandah GA, Khedher MZ, Abdel-Majeed MR, et al. Classifying and diacritizing Arabic poems using deep recurrent neural networks. Journal of King Saud University - Computer and Information Sciences. 2022, 34(6): 3775-3788. doi: 10.1016/j.jksuci.2020.12.002
9. Kumar S, Gahalawat M, Roy PP, et al. Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning. Electronics. 2020, 9(2): 374. doi: 10.3390/electronics9020374
10. Zhan G, Wang M, Zhan M. Public Opinion Detection in an Online Lending Forum: Sentiment Analysis and Data Visualization. 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA); April 2020. doi: 10.1109/icccbda49378.2020.9095690
11. Samuels A, Mcgonical J. Sentiment Analysis on Customer Responses. arXiv. 2020, arXiv:2007.02237. doi: 10.48550/arXiv.2007.02237
12. Shaukat Z, Zulfiqar AA, Xiao C, et al. Sentiment analysis on IMDB using lexicon and neural networks. SN Applied Sciences. 2020, 2(2). doi: 10.1007/s42452-019-1926-x
13. Rane AL, Kshatriya AR. Audio Opinion Mining and Sentiment Analysis of Customer Product or Services Reviews. ICDSMLA 2019. 2020, 282-293. doi: 10.1007/978-981-15-1420-3_30
14. Lakshmi Devi B, Varaswathi Bai V, Ramasubbareddy S, et al. Sentiment Analysis on Movie Reviews. Advances in Intelligent Systems and Computing. 2020, 321-328. doi: 10.1007/978-981-15-0135-7_31
15. Yin H, Yang S, Li J. Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media. Lecture Notes in Computer Science. 2020, 610-623. doi: 10.1007/978-3-030-65390-3_46
16. Arulmurugan R, Sabarmathi KR, Anandakumar H. RETRACTED ARTICLE: Classification of sentence level sentiment analysis using cloud machine learning techniques. Cluster Computing. 2017, 22(S1): 1199-1209. doi: 10.1007/s10586-017-1200-1
17. Majumder N, Poria S, Peng H, et al. Sentiment and Sarcasm Classification With Multitask Learning. IEEE Intelligent Systems. 2019, 34(3): 38-43. doi: 10.1109/mis.2019.2904691
18. Kumar A, Garg G. Sentiment analysis of multimodal twitter data. Multimedia Tools and Applications. 2019, 78(17): 24103-24119. doi: 10.1007/s11042-019-7390-1
19. Mammadli S, Huseynov S, et al. Sentiment Polarity Detection in Azerbaijani Social News Articles. Proceedings - Natural Language Processing in a Deep Learning World; 22 October 2019. doi: 10.26615/978-954-452-056-4_082
20. Cunha AAL, Costa MC, Pacheco MAC. Sentiment Analysis of YouTube Video Comments Using Deep Neural Networks. Lecture Notes in Computer Science. 2019, 561-570. doi: 10.1007/978-3-030-20912-4_51
21. Farisi AA, Sibaroni Y, Faraby SA. Sentiment analysis on hotel reviews using Multinomial Naïve Bayes classifier. Journal of Physics: Conference Series. 2019, 1192: 012024. doi: 10.1088/1742-6596/1192/1/012024
22. Rahab H, Zitouni A, Djoudi M. SANA: Sentiment analysis on newspapers comments in Algeria. Journal of King Saud University-Computer and Information Sciences. 2021, 33(7): 899-907. doi: 10.1016/j.jksuci.2019.04.012
23. Noor F, Bakhtyar M, Baber J. Sentiment Analysis in E-commerce Using SVM on Roman Urdu Text. Emerging Technologies in Computing. 2019, 213-222. doi: 10.1007/978-3-030-23943-5_16
24. Zaw M, Tandayya P. Multi-level Sentiment Information Extraction Using the CRbSA Algorithm. 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE); July 2018. doi: 10.1109/jcsse.2018.8457328
25. Appel O, Chiclana F, Carter J, et al. Successes and challenges in developing a hybrid approach to sentiment analysis. Applied Intelligence. 2017. doi: 10.1007/s10489-017-0966-4
26. Ahmad M, Aftab S, Ali I. Sentiment Analysis of Tweets using SVM. International Journal of Computer Applications. 2017, 177(5): 25-29. doi: 10.5120/ijca2017915758
27. Chao Z. RETRACTED: Research on English translation long text filtering based on LSTM semantic relevance. Microprocessors and Microsystems. 2021, 80: 103574. doi: 10.1016/j.micpro.2020.103574
28. Safaya A, Abdullatif M, Yuret D. KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media. Proceedings of the Fourteenth Workshop on Semantic Evaluation. 2020. doi: 10.18653/v1/2020.semeval-1.27
29. Afifi S, GholamHosseini H, Sinha R. Dynamic hardware system for cascade SVM classification of melanoma. Neural Computing and Applications. 2018, 32(6): 1777-1788. doi: 10.1007/s00521-018-3656-1
30. Rahat AM, Kahir A, Masum AKM. Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset. 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART); November 2019. doi: 10.1109/smart46866.2019.9117512
31. Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing. 1997, 45(11): 2673-2681. doi: 10.1109/78.650093
32. Galdi P, Tagliaferri R. Data Mining: Accuracy and Error Measures for Classification and Prediction. Encyclopedia of Bioinformatics and Computational Biology. 2019, 431-436. doi: 10.1016/b978-0-12-809633-8.20474-3
33. Abuaddous HY, Al Sokkar AA, Abualodous BI. The Impact of Knowledge Management on Organizational Performance. International Journal of Advanced Computer Science and Applications. 2018, 9(4). doi: 10.14569/ijacsa.2018.090432
34. Al Sokkar A, Law E. Validating an episodic UX model on online shopping decision making. Proceedings of the 5th ACM SIGCHI symposium on Engineering interactive computing systems; 24 June 2013. doi: 10.1145/2494603.2480301
35. Almajali DA, Omar F, Alsokkar A, et al. Enterprise resource planning success in Jordan from the perspective of IT-Business strategic alignment. Cogent Social Sciences. 2022, 8(1). doi: 10.1080/23311886.2022.2062095
DOI: https://doi.org/10.32629/jai.v7i3.940
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