Twitter data for predicting election results: Insights from emotion classification

Satish M. Srinivasan; Raghvinder S. Sangwan; Colin J. Neill; Tianhai Zu

doi:10.1109/MTS.2019.2894472

Twitter data for predicting election results: Insights from emotion classification

Engineering Division (Great Valley)

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

The advent of social media and microblogging sites has paved the way for individuals and communities to freely express their opinions, feelings, and thoughts on a variety of topics in the form of short and limited size texts such as tweets. These tweets can hold a wealth of information on how individuals communicate their thoughts, emotions (happiness, anxiety, depression, etc.) and feelings within their social network [1].

Original language	English (US)
Article number	8664560
Pages (from-to)	58-63
Number of pages	6
Journal	IEEE Technology and Society Magazine
Volume	38
Issue number	1
DOIs	https://doi.org/10.1109/MTS.2019.2894472
State	Published - Mar 2019

All Science Journal Classification (ASJC) codes

General Engineering
General Social Sciences

Access to Document

10.1109/MTS.2019.2894472

Cite this

@article{a668b18238fb4309adc88367f10d340f,

title = "Twitter data for predicting election results: Insights from emotion classification",

abstract = "The advent of social media and microblogging sites has paved the way for individuals and communities to freely express their opinions, feelings, and thoughts on a variety of topics in the form of short and limited size texts such as tweets. These tweets can hold a wealth of information on how individuals communicate their thoughts, emotions (happiness, anxiety, depression, etc.) and feelings within their social network [1].",

author = "Srinivasan, \{Satish M.\} and Sangwan, \{Raghvinder S.\} and Neill, \{Colin J.\} and Tianhai Zu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.",

year = "2019",

month = mar,

doi = "10.1109/MTS.2019.2894472",

language = "English (US)",

volume = "38",

pages = "58--63",

journal = "IEEE Technology and Society Magazine",

issn = "0278-0097",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "1",

}

TY - JOUR

T1 - Twitter data for predicting election results

T2 - Insights from emotion classification

AU - Srinivasan, Satish M.

AU - Sangwan, Raghvinder S.

AU - Neill, Colin J.

AU - Zu, Tianhai

PY - 2019/3

Y1 - 2019/3

N2 - The advent of social media and microblogging sites has paved the way for individuals and communities to freely express their opinions, feelings, and thoughts on a variety of topics in the form of short and limited size texts such as tweets. These tweets can hold a wealth of information on how individuals communicate their thoughts, emotions (happiness, anxiety, depression, etc.) and feelings within their social network [1].

AB - The advent of social media and microblogging sites has paved the way for individuals and communities to freely express their opinions, feelings, and thoughts on a variety of topics in the form of short and limited size texts such as tweets. These tweets can hold a wealth of information on how individuals communicate their thoughts, emotions (happiness, anxiety, depression, etc.) and feelings within their social network [1].

UR - https://www.scopus.com/pages/publications/85062964081

UR - https://www.scopus.com/pages/publications/85062964081#tab=citedBy

U2 - 10.1109/MTS.2019.2894472

DO - 10.1109/MTS.2019.2894472

M3 - Article

AN - SCOPUS:85062964081

SN - 0278-0097

VL - 38

SP - 58

EP - 63

JO - IEEE Technology and Society Magazine

JF - IEEE Technology and Society Magazine

IS - 1

M1 - 8664560

ER -

Twitter data for predicting election results: Insights from emotion classification

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this