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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2019 Vol.9(1): 91-97 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.1.770

Application of Artificial Intelligent in the Prediction of Consumer Behavior from Facebook Posts Analysis

Pornpimon Kachamas, Suphamongkol Akkaradamrongrat, Sukree Sinthupinyo, and Achara Chandrachai
Abstract—The undeniable fact is that online business today has increased at a very fast pace everywhere around the globe. This happens through the widely used of Social Media, especially Facebook which is the most popular platform in the world. It would be really useful for the digital marketers, if there is a certain tool that can predict the intentions of the web patrons when the brand is posting the message to communicate with their fans or followers.
The aim of this research is to develop an analytic tool which can support online vendors to predict behaviors of the patrons according to Dentsu’s AISAS perspective. An Artificial intelligent model was developed by the results from 75 specialists who evaluated the behavior that will likely occur after the comments have been posted. The results, hence, were collected and prepared for the data modelling process using the Naïve Bayes probability concept, afterwards, testing for the model’s accuracy with 10-fold cross validation technique. As the previous study indicated, Naïve Bayes technique gives the best result for the behavior analysis, which is also true with this study. The predictive model for AISAS behavior from this study can give average accuracy higher than 86 percent.
When bringing the AISAS Model to test with 30 live users who are online vendors, we can conclude that the overall results of model have been greatly appreciated and effectively satisfied. Most vendors also agreed on the ease of use, which creates high chances of business opportunities.

Index Terms—AISAS, machine learning, naive-bayes classification, social media, social media analytics.

P. Kachamas is with the School of Technopreneurship and Innovation Management, Graduate School, Chulalongkorn University, 254 Phayathai Rd., Phatumwan Bangkok, 10330 Thailand (e-mail: pornpimon.kac@student.chula.ac.th).
S. Akkaradamrongrat and S. Sinthupinyo are with the Department of Computer Engineering, Chulalongkorn University, 254 Phayathai Rd., Phatumwan Bangkok, 10330 Thailand (e-mail: 6070327421@student.chula.ac.th, sukree.s@chula.ac.th).
A. Chandrachai Author is with the Department of Commerce, Chulalongkorn University, 254 Phayathai Rd., Phatumwan Bangkok, 10330 Thailand (e-mail: achandrachai@gmail.com).

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Cite: Pornpimon Kachamas, Suphamongkol Akkaradamrongrat, Sukree Sinthupinyo, and Achara Chandrachai, "Application of Artificial Intelligent in the Prediction of Consumer Behavior from Facebook Posts Analysis," International Journal of Machine Learning and Computing vol. 9, no. 1, pp. 91-97, 2019.

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