Automatic social media data extraction

被引:0
|
作者
机构
[1] Kee, Estelle Xin Ying
[2] Hong, Jer Lang
来源
| 1600年 / Science and Engineering Research Support Society卷 / 09期
关键词
Data mining - Extraction - Behavioral research - Decision making - Semantics - Social networking (online);
D O I
10.14257/ijdta.2016.9.8.05
中图分类号
学科分类号
摘要
Opinions, the key influencer of human behavior and activity is ranked as of one of the strong factors that determine the effectiveness of one's strategy and approach in terms of influential power and trend setting capabilities. This highlights the importance of sentiment analysis done upon the extracted data. Today, statistics have shown significantly that most opinions can be obtained via many social media platforms. Social media has provided a convenient platform for web users to comfortably share their thoughts and to boldly voice up. Having to process such huge amount of data, it is proposed that automated sentiment analysis is done when extracting social media data. Using an effective algorithm which produces meaningful information from raw data, the possibilities of venturing deeper into areas like decision making and influential thinking are simply limitless. © 2016 SERSC.
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