Analyzing unstructured Facebook social network data through web text mining: A study of online shopping firms in Turkey

被引:5
|
作者
Kahya-Ozyirmidokuz, Esra [1 ]
机构
[1] Erciyes Univ, Fac Econ & Adm Sci, Kayseri, Turkey
关键词
text mining; web mining; Facebook; knowledge discovery in databases; data mining; information extraction; online shopping; Turkey; EXTRACTION;
D O I
10.1177/0266666914528523
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
The large amounts of Facebook social network data which are generated and collected need to be analyzed for valuable decision making information about shopping firms in Turkey. In addition, analyzing social network data from outside the firms becomes a critical business need for the firms which actively use Facebook. To have a competitive advantage, firms must translate social media texts into something more quantitative to extract information. In this study, web text mining techniques are used to determine popular online shopping firms' Facebook patterns. For this purpose, 200 popular Turkish companies' web URLs are used. Web text mining through natural language processing techniques is examined. Similarity analysis and clustering are done. Consequently, the clusters of the Facebook websites and their relationships and similarities of the firms are obtained.
引用
收藏
页码:70 / 80
页数:11
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