Tweet-based Target Market Classification Using Ensemble Method

Muhammad Adi Khairul Anshary, Bambang Riyanto Trilaksono

Abstract


Target market classification is aimed at focusing marketing activities on the right targets. Classification of target markets can be done through data mining and by utilizing data from social media, e.g. Twitter. The end result of data mining are learning models that can classify new data. Ensemble methods can improve the accuracy of the models and therefore provide better results. In this study, classification of target markets was conducted on a dataset of 3000 tweets in order to extract features. Classification models were constructed to manipulate the training data using two ensemble methods (bagging and boosting). To investigate the effectiveness of the ensemble methods, this study used the CART (classification and regression tree) algorithm for comparison. Three categories of consumer goods (computers, mobile phones and cameras) and three categories of sentiments (positive, negative and neutral) were classified towards three target-market categories. Machine learning was performed using Weka 3.6.9. The results of the test data showed that the bagging method improved the accuracy of CART with 1.9% (to 85.20%). On the other hand, for sentiment classification, the ensemble methods were not successful in increasing the accuracy of CART. The results of this study may be taken into consideration by companies who approach their customers through social media, especially Twitter.

Full Text:

PDF

References


Wells, W., Burnett, J. & Moriarty, S., Advertising: Principles and Practice. Fifth Edition, Prentice-Hall, New Jersey, United States 2000.

Aaker, D.A., Myers, J.G. & Batra, R., Advertising Management, Prentice-Hall, New Jersey, United States, 1996.

Xiang li, S.Y. & Niu, Y-P Targeting Market Selection Based-On Fuzzy Preference Relation, Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 26-29 August 2004, no. August, pp. 2390-2392, 2004.

Shahrokhi,N., Dehzad, R. & Sahami, S., Targeting Customers with Data Mining Techniques: Classification, International Conference on User Science and Engineering (i-USEr), pp. 212-215, 2011.

Canali, C., Casolari, S. & Lancellotti, R., A Quantitative Methodology to Identify Relevant Users in Social Networks,” IEEE Online Journal., http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5730307&tag=1 (22 April 2014).

Guoxiang, L. & Zhiheng, Q., Data Mining Applications in Marketing Strategy, Third International Conference on Intelligent System Design and Engineering Applications, Hong Kong, China, pp. 518-520, Jan. 2013.

Liu, L., Yang, Z. & Benslimane, Y., Conducting Efficient and Cost-Effective Targeted Marketing Using Data Mining Techniques, 2013 Fourth Global Congress on Intelligent Systems, pp. 102-106, Dec. 2013.

Khan, M.A. & Khan, A., A Novel Learning Method to Classify Data Streams in the Internet of Things, National Software Engineering Conference (NSEC), Islamabad, Pakistan, pp. 61-66, 2014.

Yao, Y.Y. & Zhong, N., Mining Market Value Functions for Targeted Marketing, 25th Annual International Computer Software and Application Conference, Chicago, Ilinois, United States, pp. 517-522, 2001.

Ahn, H., Joon, J., Joo, K. & Ha, D., Expert Systems with Applications Facilitating Cross-Selling in a Mobile Telecom Market to Develop Customer Classification Model Based on Hybrid Data Mining Techniques, Expert Systems With Applications, 38(5), pp. 5005-5012, 2011.

Wan, Y., An Ensemble Sentiment Classification System of Twitter Data for Airline Services Analysis, Thesis, Dalhousie University Halifax, Nova Scotia, Canada, March, 2015.

Martínez-Cámara, E., Gutiérrez-Vázquez, Y. & Fernández, J., Ensemble Classifier for Twitter Sentiment Analysis, http://wordpress.let.vupr.nl/ nlpapplications/files/2015/06/WNACP-2015_submission_6.pdf (4 December 2015).

J. Manalu, “Bhinneka.com targeting Turnover Grows 40%,” Bisnis Indonesia, 2014, http://industri.bisnis.com/read/20140213/12/202921/ bhinneka.com-targetkan-omzet-tumbuh-40. (Text in Indonesian, accessed on 13 February 2014).

Naradhipa, A.R. Sentiment Classification for Indonesian Messages on Social Media use Noisy Text for Preprocessing, Thesis, School of Electrical Enginering and Informatics, Bandung Institute of Technology, Bandung, Indonesia, 2012.

Asian, J., Williams, H.E. & Tahaghoghi, S.M.M., Stemming Indonesian, 28th Australasian Computer Science Conference (ACSC2005), The University of Newcastle, Australia, 2005.

Zhou, Z-H., Ensemble Methods Foundations and Algorithms, CRC Press, Boca Raton, Florida, United States, 2012.

Dietterich, T.G., Ensemble Methods in Machine Learning, In First International Workshop on Multiple Classifier Systems, J. KITTLER AND F. ROLI, Eds. Lecture Notes in Computer Science, New York, United States, Springer Verlag, pp. 1-15, 2000.

Liu, B., Web Data Mining, Exploring Hyperlinks, Contents and Usage Data, Second Edition, Springer, New York, United States, 2011.




DOI: http://dx.doi.org/10.5614%2Fitbj.ict.res.appl.2016.10.2.3

Refbacks

  • There are currently no refbacks.


Contact Information:

ITB Journal Publisher, LPPM – ITB, 

Center for Research and Community Services (CRCS) Building Floor 7th, 
Jl. Ganesha No. 10 Bandung 40132, Indonesia,

Tel. +62-22-86010080,

Fax.: +62-22-86010051;

e-mail: jictra@lppm.itb.ac.id.