Text Mining Algorithms in Social Media

Authors

  • Kousalyadevi .B
  • Dr. A. Finny Belwin
  • Dr.A. Linda Sherin
  • Dr. Antony Selvadoss Thanamani

DOI:

https://doi.org/10.37591/joces.v10i3.762

Keywords:

Social media, text mining, blogging, categories, text analysis, text mining algorithms, K-means, K-nearest Neighbor (KNN), support vector machines (SVM), decision trees

Abstract

Abstract

Social networks are wealthy in different sorts of substance, for example, texts and multimedia. The capacity to apply text mining calculations viably with regards to content information is basic for a wide assortment of uses. Interpersonal organizations require text mining calculations for a wide assortment of uses, for example, watchword search, characterization, and bunching. While search and characterization are notable applications for a wide assortment of situations, interpersonal organizations have a lot more extravagant structure both as far as content and connections. A great part of the work in the region utilizes either absolutely the content substance or simply the linkage structure. Be that as it may, numerous ongoing calculations utilize a mix of linkage and substance data for mining purposes. By and large, things being what they are, the utilization of a mix of linkage and substance data gives substantially more successful outcomes than a system which depends absolutely on both of the two. This paper gives an overview of social media, categories of social media, and challenges of text analysis in social media, six key tips for using text mining in social media and the algorithms that are used for text mining.

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Published

2021-02-09

Issue

Vol. 10 No. 3 (2020)

Section

Articles

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