Feature Engineering for Twitter-based Applications
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Social media websites have become extremely popular among online users in recent years. Surveys performed by Pew Research Center in 2016 claimed that social networking sites are visited by 69% of the total U.S. population where 76% of them daily check those websites1. These online activities generate large amounts of user-generated content that can be mined to understand user interests and recommend products to online users, develop targeted marketing campaigns for products, understand the user’s perspectives of a product, etc. Among many online social networking websites, Twitter has gained popularity due to the fact that users can follow any other user’s activities,by accessing their short text messages, called ‘tweets’, posted to the Twitter network. For example, Twitter users can follow their favorite celebrities to earn what they share public ally, in real-time. Currently, Twitter has grown to a social network of 328 million active users who post around 500 million messages collectively everyday2
Al-Olimat, H. S.,
Yazdavar, A. H.,
& Thirunarayan, K.
(2018). Feature Engineering for Twitter-based Applications. Feature Engineering for Machine Learing and Data Analytics, 5-39.