Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion
Downloads
Background: Market prediction is an important thing that needs to be analyzed deeply. Business intelligence becomes an important analysis procedure for analyzing the market demand and satisfaction. Since business intelligence needs a deep analysis, sentiment analysis becomes a powerful algorithm for analyzing customer review regarding to the business intelligence analysis.
Objective: In this study, we perform a sentiment analysis for identifying the business intelligence analysis in GO-JEK.
Methods: We use Twitter posts collected from the Twint library which consists of 3111 tweets. Since the dataset did not provide a ground truth, we perform Microsoft Text Analytic for determining positive, neutral, and negative sentiment. Before applying Microsoft Text Analytic, we conduct a pre-processing step to remove the unwanted data such as duplicate tweets, image, website address, etc.
Results: According to the Microsoft Text Analytic, the results are 666 positive sentiment numbers, 2055 neutral sentiment numbers, and 127 negative sentiment numbers.
Conclusion: According to these results, we conclude that most GO-JEK customers are satisfied with the GO-JEK services. In this research, we also develop classification model to predict the sentiment analysis of new data. We use some classifier algorithms such as Decision Tree, Naí¯ve Bayes, Support Vector Machine and Neural Network. In the result, the system shows that the decision tree provides the best performance.
P. Wazurkar, R. S. Bhadoria and D. Bajpai, "Predictive analytics in data science for business intelligence solutions," in 7th International Conference on Communication Systems and Network Technologies (CSNT), Nagpur, 2017.
G. Shmueli and O. Koppius, "Predictive analytics in information systems research," MIS Quarterly, vol. 35, no. 3, pp. 553-572, 2011.
R. Vedder, M. Vanecek, C. Guynes and J. Cappel, "CEO and CIO Perspectives on Competitive Intelligence," Communications of the ACM, vol. 42, no. 8, pp. 108-116, 1999.
L. Zhang and B. Liu, "Sentiment Analysis and Opinion Mining," in Encyclopedia of Machine Learning and Data Mining, Boston, Springer, 2017.
Ganesan, Kavita and G. Zhou, "Linguistic Understanding of Complaints and Praises in User Reviews," in NAACL-HLT, 2016.
J. Suter, "Airport image: An exploratory study of McCarran International Airport," Journal of Air Transport Management, vol. 67, pp. 72-84, 2018.
N. Haghighat, "Airline service quality evaluation: A review of concepts and models," Economics, Management, and Sustainability, vol. 2, no. 2, pp. 31-47, 2017.
V. Bogicevic, W. Yang, A. Bilgihan and Bujisic, "Airport service quality drivers of passenger satisfaction," Tourism Rev., vol. 68, no. 4, p. 3–18, 2013.
Q. Ye, Z. Z. and L. B., "Sentiment classification of online reviews to travel destinations by supervised machine learning approaches," Expert Systems with Applications, vol. 36, p. 6527–6535, 2009.
C. V., W. R and S. Purwanto, "Komparasi Algoritma Klasifikasi Machine Learning Dan Feature Selection pada Analisis Sentimen Review Film," Journal of Intelligent Systems, vol. 1, no. 1, pp. 55-59, 2015.
S. Tan and J. Zhang, "An empirical study of sentiment analysis for chinese documents," Expert Systems with Applications, vol. 34, no. 4, p. 2622–2629, 2015.
S. Lidya, O. Sitompul and S. Efendi, "Sentimen analysis pada teks bahasa Indonesia menggunakan Support Vector Machine (SVM) dan KNearest Neighbor (K-NN)," in Seminar Nasional Teknologi Informasi dan Komunikasi, 2015.
A. Balahur, "Sentiment Analysis in Social Media Texts," in Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Atlanta, Georgia, 2013.
C. Bhadane, H. Dalal and H. Doshi, "Sentiment analysis: Measuring opinions," Procedia - Procedia Computer Science, vol. 45, p. 808–814, 2015.
A. Lestari and D. Karolita, "Summarizing Netizens' Sentiments Towards the 1st Indonesian Presidential Debate using Lexicon Sentiment Analysis," in 9th Annual Basic Science International Conference 2019 (BaSIC 2019), 2019.
A. Koneru, N. B. N. S. R. Bhavani, K. P. Rao, G. S. Prakash, I. P. Kumar and V. V. Kumar, "Sentiment Analysis on top five Cloud Service Providers in the Market," in 2nd International Conference on Trends in Electronics and Informatics (ICOEI 2018), 2018.
D. A. Ivor, D. Do, R. Ge and S. I. Ahamed, "A Reference Architecture for Social Media Intelligence Applications in the Cloud," in IEEE 39th Annual International Computers, Software & Applications Conference, 2015.
C. Arijit and W. Perrizo, "Investor Classification and Sentiment Analysis," in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016.
L. M. Qaisi and I. Aljarah, "A Twitter Sentiment Analysis for Cloud Providers: A Case Study of Azure vs. AWS," in 7th International Conference on Computer Science and Information Technology (CSTT), 2016.
Microsoft Azure, "What is Text Analytics API?," [Online]. Available: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview. [Accessed 11 December 2019].
A. I, S. D, N. F and H. A. N., "Utilizing hashtags for sentiment analysis of tweets in the political domain," in 9th Int. Conf. on Machine Learning and Computing - ICMLC 2017, 2017.
J.Ren, S.D.Lee, X.Chen, B.Kao, R.Cheng and D.Cheung, "Naive Bayes Classification of Uncertain Data," in Ninth IEEE International Conference on Data Mining (ICDM '09), 2009.
M. O. Pratama, W. Satyawan, R. Jannati, B. Pamungkas, Raspiani, M. E. Syahputra and I. Neforawati, "The sentiment analysis of Indonesia commuter line using machine learning based on twitter data," in International Conference of Computer and Informatics Engineering (IC2IE), 2019.
G. Kaur and K. Bajaj, "News Classification and Its Techniques: A Review," IOSR Journal of Computer Engineering (IOSR-JCE), vol. 18, no. 1, pp. 22-26, 2016.
C. Hong, L. Ziwei, Y. Cuitin, L. Jie and Y. Air, "A Shallow Neural Network based Short Text Classifier for Medical Community Question Answering System," in Proceedings of 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, 2018.
X. B., G. X., Y. Ye. and J. Cheng, "An Improved Random Forest Classifier for Text Categorization," JCP, vol. 7, p. 2913–2920, 2012.
S. Safavian and D. Landgrebe, "A survey of decision tree classifier methodology," IEEE Trans. Syst. Man Cybern., vol. 21, p. 660–674, 1991.
C. Giovanelli, X. Liu, S. Sierla, V. Vyatkin and R. Ichise, "Towards an aggregator that exploits big data to bid on frequency containment reserve market," in 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), Beijing, China, 2017.
K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes and D. Brown, "Text Classification Algorithms: A Survey," Information, vol. 10, no. 4, p. 150, 2019.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
