UM
Pattern Mining Approaches Used in Social Media Data
Chaki,Jyotismita1; Dey,Nilanjan2; Panigrahi,B. K.3; Shi,Fuqian4; Fong,Simon James5; Sherratt,R. Simon6
2020-12-01
Source PublicationInternational Journal of Uncertainty, Fuzziness and Knowlege-Based Systems
ISSN0218-4885
Volume28Issue:Supp02Pages:123-152
AbstractSocial media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structured-unstructured and formal-informal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites (Twitter, Facebook, YouTube, LinkedIn, etc.). People are increasingly involved in and dependent on social media networks for data, news and opinions of other handlers on a variety of topics. The strong dependence on social media network sites contributes to enormous data generation characterized by three issues: scale, noise, and variety. Such problems also hinder social network data to be evaluated manually, resulting in the correct use of statistical analytical methods. Mining social media data can extract significant patterns that can be advantageous for consumers, users, and business. Pattern mining offers a wide variety of methods to detect valuable knowledge from huge datasets, such as patterns, trends, and rules. In this work, data was collected comprised of users' opinions and sentiments and then processed using a significant number of pattern mining methods. The results were then further analyzed to attain meaningful information. The aim of this paper is to deliver a summary and a set of strategies for utilizing the ubiquitous pattern mining approaches, and to recognize the challenges and future research guidelines of dealing out social media data.
Keywordclassification clustering feature extraction feature selection pattern mining pre-processing Social media
DOI10.1142/S021848852040019X
URLView the original
Language英语
Fulltext Access
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorChaki,Jyotismita
Affiliation1.School of Information Technology and Engineering,Vellore Institute of Technology,Vellore,India
2.Department of Computer Science and Engineering,Jis University,Kolkata,India
3.Department of Electrical Engineering,Iit,Delhi,India
4.Cancer Institute of New Jersey,Rutgers University,United States
5.Department of Computer and Information Science,University of Macau,Macao
6.Department of Biomedical Engineering,University of Reading,United Kingdom
Recommended Citation
GB/T 7714
Chaki,Jyotismita,Dey,Nilanjan,Panigrahi,B. K.,et al. Pattern Mining Approaches Used in Social Media Data[J]. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems,2020,28(Supp02):123-152.
APA Chaki,Jyotismita,Dey,Nilanjan,Panigrahi,B. K.,Shi,Fuqian,Fong,Simon James,&Sherratt,R. Simon.(2020).Pattern Mining Approaches Used in Social Media Data.International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems,28(Supp02),123-152.
MLA Chaki,Jyotismita,et al."Pattern Mining Approaches Used in Social Media Data".International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems 28.Supp02(2020):123-152.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chaki,Jyotismita]'s Articles
[Dey,Nilanjan]'s Articles
[Panigrahi,B. K.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chaki,Jyotismita]'s Articles
[Dey,Nilanjan]'s Articles
[Panigrahi,B. K.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chaki,Jyotismita]'s Articles
[Dey,Nilanjan]'s Articles
[Panigrahi,B. K.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.