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Detecting the Online Shopping Factors Using the Arab Tweets on Media Technology


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DOI: https://doi.org/10.15866/irecap.v10i3.19230

Abstract


With the popularization of the internet and media technology and the rapid adaption to the development of information technology, the online shopping has been accepted by millions of customers as a new model of shopping. It has been applied to different domains such as electronics, books and clothing marketing. Thus, it has led to the growth of hundreds of online shopping sites around the world and has created competitive electronic commerce. This paper analyzes the influencing factors on consumers of online shopping in the Arab world using Arabic tweets. Several classification algorithms have been used, such as support vector machine, naive Bayes, random forest, voting classifier and decision tree in Python and Weka. The results show that around 51% of users say that the online shopping is better than the traditional one based on the tweets in dataset. In addition, it has been found out that the random forest is the best algorithm in Python compared to other ones with an accuracy of 93.7% in 0.4 test size.
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Keywords


Machine Learning; Online Shopping; Data Mining Tools; Hold-Out Method; Influencing Factors; Media Technology

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References


M. H. Moshrefjavadi, H. R. Dolatabadi, M. Nourbakhsh, A. Poursaeedi, A. Asadollahi, An Analysis of Factors Affecting on Online Shopping Behavior of Consumers, International Journal of Marketing Studies, Volume 4, (Issue 5), October 2012.
https://doi.org/10.5539/ijms.v4n5p81

P. Chandra and J. Chen, Taming the Amazon, Proceedings of the Tenth International Conference on Information and Communication Technologies and Development - ICTDX 19, 2019.
https://doi.org/10.1145/3287098.3287105

R. A. E.-D. Ahmeda, M. E. Shehaba, S. Morsya, N. Mekawiea, Performance Study of Classification Algorithms for Consumer Online Shopping Attitudes and Behavior Using Data Mining, 2015 Fifth International Conference on Communication Systems and Network Technologies, 2015.
https://doi.org/10.1109/csnt.2015.50

Y.-P. Chiu, S.-K. Lo, A.-Y. Hsieh, and Y. Hwang, Exploring why people spend more time shopping online than in offline stores, Computers in Human Behavior, Volume 95, 2019, Pages 24–30.
https://doi.org/10.1016/j.chb.2019.01.029

A. Kumar, M. Saini, P. Hans, Factors influencing online shopping behavior of university students, International Journal of System Assurance Engineering and Management, Volume 10, (Issue 4), September 2019, Pages 861–865.
https://doi.org/10.1007/s13198-019-00797-7

P. Jiang, D. B. Jones, An Exploratory Study of Factors Affecting Consumer International Online Shopping Behavior, Encyclopedia of E-Commerce Development, Implementation, and Management, 2016, Pages 1627–1642.
https://doi.org/10.4018/978-1-4666-9787-4.ch115

Y. J. Lim, A. Osman, S. N. Salahuddin, A. R. Romle, S. Abdullah, Factors Influencing Online Shopping Behavior: The Mediating Role of Purchase Intention, Procedia Economics and Finance, Volume 35, 2016, Pages 401–410.
https://doi.org/10.1016/s2212-5671(16)00050-2

T. M. Nisar, G. Prabhakar, What factors determine e-satisfaction and consumer spending in e-commerce retailing?, Journal of Retailing and Consumer Services, Volume 9, 2017, Pages 135– 144.
https://doi.org/10.1016/j.jretconser.2017.07.010

T. P. Y. Monsuwé, B. G. Dellaert, and K. D. Ruyter, What drives consumers to shop online? A literature review, International Journal of Service Industry Management, Volume 15, (Issue 1), 2004, Pages 102–121.
https://doi.org/10.1108/09564230410523358

M. Chang, W. Cheung, and V. Lai, Literature derived reference models for the adoption of online shopping, Information & Management, Volume 42, (Issue 4), 2005, Pages. 543–559.
https://doi.org/10.1016/s0378-7206(04)00051-5

A. Ayedh, G. TAN, K. Alwesabi and H. Rajeh, The Effect of Preprocessing on Arabic Document Categorization, Algorithms, Volume 9, (Issue 2), 2016, Pages 1-27.
https://doi.org/10.3390/a9020027

https://www.cs.waikato.ac.nz/ml/weka/.

Umadevi, Sentiment Analysis Using Weka, International Journal of Engineering Trends and Technology (IJETT), Volume 18, (Issue 4), 2014, Pages 181-183.
https://doi.org/10.14445/22315381/ijett-v18p236

P. Han, D. Wang, Q. Zhao, The research on Chinese document clustering based on WEKA, 2011 International Conference on Machine Learning and Cybernetics, 2011.
https://doi.org/10.1109/icmlc.2011.6016955

Saravanan, R.A., Rajesh Babu, M. Enhanced text mining approach based on ontology for clustering research project selection. J Ambient Intell Human Comput (2017).
https://doi.org/10.1007/s12652-017-0637-7

https://wekatutorial.com/.

A. Kulkarni and A. Shivananda, Converting Text to Features, Natural Language Processing Recipes, 2019, Pages 67-96.
https://doi.org/10.1007/978-1-4842-4267-4_3

M. Allahyari, S, Pouriyeh, M. Assefi, S. Safaei, E. D. Trippe, J. B. Gutierrez, K. Kochut, A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques, arXiv:1707.02919 , July 2017.
https://doi.org/10.14569/ijacsa.2017.081052

R. Al-khurayji, A. Sameh, An Effective Arabic Text Classification Approach Based on Kernel Naive Bayes Classifier, International Journal of Artificial Intelligence & Applications, Volume 8, (Issue 6), 2017, Pages 01-10.
https://doi.org/10.5121/ijaia.2017.8601

S. Bachhety, S. Dhingra, R. Jain, N. Jain, Improved Multinomial Naïve Bayes Approach for Sentiment Analysis on Social Media, Social Science Research Network, Rochester, NY, SSRN Scholarly Paper ID 3363601, 2018.

A. A. Farisi, Y. Sibaroni, S. A. Faraby, Sentiment analysis on hotel reviews using Multinomial Naïve Bayes classifier, J. Phys.: Conf. Ser., Volume 1192, 2019.
https://doi.org/10.1088/1742-6596/1192/1/012024

G. Tsoumakas, I. Katakis, Multi-Label Classification, International Journal of Data Warehousing and Mining, Volume 3, (Issue 4), 2007, Pages 1-13.
https://doi.org/10.4018/jdwm.2007070101

Y. K. Qawqzeh, M. M. Otoom, F. Al-Fayez, I. Almarashdeh, G. Jaradat, A Proposed Decision Tree Classifier for Atherosclerosis Prediction and Classification, International Journal of Computer Science and Network Security, Volume 19, (Issue 12), 2019.

S. Sahoo, A. Subudhi, M. Dash, S. Sabut, Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and Decision Tree Algorithm, IJAC, Volume 17, (Issue 2), 2020, Pages 1-11.
https://doi.org/10.1007/s11633-019-1219-2

R. Duwairi, R. Marji, N. Sha'ban, S. Rushaidat, Sentiment Analysis in Arabic tweets, 2014 5th International Conference on Information and Communication Systems (ICICS), 2014.
https://doi.org/10.1109/iacs.2014.6841964

S. Tong, D. Koller, Support Vector Machine Active Learning with Applications to Text Classification, JMLR, 2001, Pages 45-66.

D. Sculley, G. Wachman, Relaxed online SVMs for spam filtering, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07, 2007.
https://doi.org/10.1145/1277741.1277813

R. D. Howsalya Devi, A. Bai, N. Nagarajan, A novel hybrid approach for diagnosing diabetes mellitus using farthest first and support vector machine algorithms, Obesity Medicine, Volume 17, 2020.
https://doi.org/10.1016/j.obmed.2019.100152

M. Pal, Random forest classifier for remote sensing classification, International Journal of Remote Sensing Volume 26, (Issue 1), 2005, Pages 217-222.
https://doi.org/10.1080/01431160412331269698

N. Arora, P. D. Kaur, A Bolasso based consistent feature selection enabled random forest classification algorithm: An application to credit risk assessment, Applied Soft Computing, Volume 86, 2020.
https://doi.org/10.1016/j.asoc.2019.105936

J. Matoušek and D. Tihelka, Glottal Closure Instant Detection from Speech Signal Using Voting Classifier and Recursive Feature Elimination, Interspeech 2018, 2018.
https://doi.org/10.21437/interspeech.2018-1147

D. Mungra, A. Agrawal, and A. Thakkar, A Voting-Based Sentiment Classification Model, in Intelligent Communication, Control and Devices, Singapore, 2020, Pages 551–558.
https://doi.org/10.1007/978-981-13-8618-3_57

B. Chen, X. Chen and W. Xing, "Twitter Archeology" of learning analytics and knowledge conferences, Proceedings of the Fifth International Conference on Learning Analytics And Knowledge - LAK '15, 2015.
https://doi.org/10.1145/2723576.2723584

Moloi, K., Jordaan, J., Hamam, Y., The Development of a High Impedance Fault Diagnostic Scheme on Power Distribution Network, (2020) International Review of Electrical Engineering (IREE), 15 (1), pp. 69-79.
https://doi.org/10.15866/iree.v15i1.17074

Marrugo Cardenas, N., Amaya Hurtado, D., Ramos Sandoval, O., Comparison of Multi-Class Methods of Features Extraction and Classification to Recognize EEGs Related with the Imagination of Two Vowels, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (5), pp. 398-405.
https://doi.org/10.15866/irecap.v8i5.12709


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