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Pattern Mining Approaches Used in Social Media Data
Jyotismita Chaki1; Nilanjan Dey2; B. K. Panigrahi3; Fuqian Shi4; Simon James Fong5; R. Simon Sherratt6
Source PublicationInternational Journal of Uncertainty, Fuzziness and Knowlege-Based Systems

Social 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.

KeywordSocial Media Pattern Mining Classification Clustering Pre-processing Feature Extraction Feature Selection
URLView the original
Indexed BySCIE ; SSCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000603590300008
Scopus ID2-s2.0-85098479248
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Document TypeJournal article
Corresponding AuthorJyotismita Chaki
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
Jyotismita Chaki,Nilanjan Dey,B. K. Panigrahi,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 Jyotismita Chaki,Nilanjan Dey,B. K. Panigrahi,Fuqian Shi,Simon James Fong,&R. Simon Sherratt.(2020).Pattern Mining Approaches Used in Social Media Data.International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems,28(Supp02),123-152.
MLA Jyotismita Chaki,et al."Pattern Mining Approaches Used in Social Media Data".International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems 28.Supp02(2020):123-152.
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