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Classifying Short Text in Social Media: Twitter as Case Study

by Faris Kateb, Jugal Kalita
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 9
Year of Publication: 2015
Authors: Faris Kateb, Jugal Kalita
10.5120/19563-1321

Faris Kateb, Jugal Kalita . Classifying Short Text in Social Media: Twitter as Case Study. International Journal of Computer Applications. 111, 9 ( February 2015), 1-12. DOI=10.5120/19563-1321

@article{ 10.5120/19563-1321,
author = { Faris Kateb, Jugal Kalita },
title = { Classifying Short Text in Social Media: Twitter as Case Study },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 9 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number9/19563-1321/ },
doi = { 10.5120/19563-1321 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:47:23.615222+05:30
%A Faris Kateb
%A Jugal Kalita
%T Classifying Short Text in Social Media: Twitter as Case Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 9
%P 1-12
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the huge growth of social media, especially with 500 million Twitter messages being posted per day, analyzing these messages has caught intense interest of researchers. Topics of interest include micro-blog summarization, breaking news detection, opinion mining and discovering trending topics. In information extraction, researchers face challenges in applying data mining techniques due to the short length of tweets as opposed to normal text with longer length documents. Short messages lead to less accurate results. This has motivated investigation of efficient algorithms to overcome problems that arise due to the short and often informal text of tweets. Another challenge that researchers face is stream data, which refers to the huge and dynamic flow of text generated continuously from social media. In this paper, we discuss the possibility of implementing successful solutions that can be used to overcome the inconclusiveness of short texts. In addition, we discuss methods that overcome stream data problems.

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Index Terms

Computer Science
Information Sciences

Keywords

Social Media Mining Short Text Classification Stream Data

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