A Comparative Analysis of Top 5 Fast Food Restaurants through Text Mining

被引:0
|
作者
Ali, Tahir [1 ]
Ahmad, Imran [2 ]
Adil, Arooj Aslam [3 ]
Kamal, Shahid [4 ]
机构
[1] Gulf Univ Sci & Technol, Kuwait, Kuwait
[2] Riphah Int Univ Lahore Pakistan, Lahore, Pakistan
[3] Univ Cent Punjab, Lahore, Pakistan
[4] Gomal Univ, ICIT, Dikhan, Pakistan
来源
2018 5TH INTERNATIONAL CONFERENCE ON BEHAVIORAL, ECONOMIC, AND SOCIO-CULTURAL COMPUTING (BESC) | 2018年
关键词
Social Media; Text Mining; Restaurants; Analysis; WEB;
D O I
10.1109/BESC.2018.00054
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Text mining is a logical way of mining content and to know about feelings of users. Social media plays a vital role in letting people know about each other views. An ever increasing number of different online integration brands are working on Facebook, Instagram, Twitter and other social networks to furnish several directions and collaborate with multiple consumers. Social media helps different companies to enhance their businesses and get audience feedback for the betterment of their business timeline. Consequently, a lot of customer produced content is uninhibitedly accessible via web-based networking media sites. To increment upper hand and satisfactorily evaluate the focused condition of many companies, they need to work on and analyze the content that will affect their competitors who are also working on social integration networks. This paper represents a comparative analysis of five different social media pages and we have applied text mining to extract data from Facebook or Twitter of five fast food restaurants i.e. KFC, McDonald's, Burger King, Hardees and Howdy.
引用
收藏
页码:225 / 232
页数:8
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