Feature Engineering for Twitter-based Applications

Sanjaya Wijeratne; Amit Sheth; Shreyansh Bhatt; Lakshika Balasuriya; Hussein S. Al-Olimat; Manas Gaur; Amir Hossein Yazdavar; Krishnaprasad Thirunarayan

Feature Engineering for Twitter-based Applications

Sanjaya Wijeratne, Amit Sheth, Shreyansh Bhatt, Lakshika Balasuriya, Hussein S. Al-Olimat, Manas Gaur, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan

Wright State University

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

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 everyday ²

Original language	American English
Title of host publication	Feature Engineering for Machine Learing and Data Analytics
State	Published - Jan 1 2018

Disciplines

Bioinformatics
Communication
Communication Technology and New Media
Computer Sciences
Databases and Information Systems
Life Sciences
OS and Networks
Physical Sciences and Mathematics
Science and Technology Studies
Social and Behavioral Sciences

Cite this

@inbook{02acaf7dabf04f8ba0376b02ca9a70ab,

title = "Feature Engineering for Twitter-based Applications",

abstract = " 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{\textquoteright}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{\textquoteright}s activities,by accessing their short text messages, called {\textquoteleft}tweets{\textquoteright}, 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 everyday 2 ",

author = "Sanjaya Wijeratne and Amit Sheth and Shreyansh Bhatt and Lakshika Balasuriya and Al-Olimat, {Hussein S.} and Manas Gaur and Yazdavar, {Amir Hossein} and Krishnaprasad Thirunarayan",

year = "2018",

month = jan,

day = "1",

language = "American English",

booktitle = "Feature Engineering for Machine Learing and Data Analytics",

}

TY - CHAP

T1 - Feature Engineering for Twitter-based Applications

AU - Wijeratne, Sanjaya

AU - Sheth, Amit

AU - Bhatt, Shreyansh

AU - Balasuriya, Lakshika

AU - Al-Olimat, Hussein S.

AU - Gaur, Manas

AU - Yazdavar, Amir Hossein

AU - Thirunarayan, Krishnaprasad

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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 everyday 2

AB - 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 everyday 2

UR - https://corescholar.libraries.wright.edu/knoesis/1163

M3 - Chapter

BT - Feature Engineering for Machine Learing and Data Analytics

ER -

Feature Engineering for Twitter-based Applications

Abstract

Disciplines

Other files and links

Cite this