Summary
International Technical Conference on Circuits/Systems, Computers and Communications
2016
Session Number:T2-4
Session:
Number:4840
Thai Social Media Alert System for Business
Supatta Viriyavisuthisakul, Parinya Sanguansat, Pisit Charnkeitkong, Choochart Haruechaiyasak ,
pp.531-534
Publication Date:2016/7/10
Online ISSN:2188-5079
Summary:
Nowadays, social media has exponential growth of information generated by social interaction. In this way, social media has a huge impact on how businesses connect with customers. Pantip is the one of the most popular web board in Thailand in which the customers post their feedbacks about products and services. Among these feedbacks, some of them affects the business in negative way. To solve this problem, business should have a system that can alert their customer's negative feedbacks on social media. If the business can quickly deal with these negative feedbacks, the problems will be managed more easily. Therefore, many businesses can take advantage of this information for market analysis that can increase their opportunities. In this paper, the social media tools, that can utilized the data from social medias, are surveyed from 2005 to present. Each system has different features, but none of them cannot meet the business requirements, especially when the business cannot deal with the problem immediately because the negative sentiments are incorrectly classified and not alert. This paper proposes the social media alert system based on the business requirements. The machine learning technique is applied here to determine which information should be alerted to business. The system monitors the data in Pantip that relate to the business by keywords. After the data were collected and preprocessed, feature vectors are extracted by Term Frequency-Inversed Document Frequency (TF-IDF) before feeding to Support Vector Machine (SVM). Experimental results show that it can achieved the good accuracy rate and also in terms of the sensitivity and specificity.