Social Media Behavioural Analysis With Document Tree-Based Rule Mining and Document Clustering
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2021 by IJETT Journal|
|Year of Publication : 2021|
|Authors : S. Geetha, Dr. R. Kaniezhil
|DOI : 10.14445/22315381/IJETT-V69I1P213|
MLA Style: S. Geetha, Dr. R. Kaniezhil. "Social Media Behavioural Analysis With Document Tree-Based Rule Mining and Document Clustering" International Journal of Engineering Trends and Technology 69.1(2021):85-91.
APA Style:S. Geetha, Dr. R. Kaniezhil. Social Media Behavioural Analysis With Document Tree-Based Rule Mining and Document Clustering International Journal of Engineering Trends and Technology, 69(1), 85-91.
Twitter in Social media has become an important part of regular lives. This media provides a list of trending real-time topics where most information is hard to comprehend, making it imperative to classify for finding useful information. A large database with real-time information is generated on Twitter. Twitter tweets are a storehouse of text and can reflect human emotions and feelings. Hidden information found in this data can be used for multiple purposes. However, the results depend on choosing a proper feature set. Human biological, pharmacological, and experiential factors influence their behavior. Behavior Analysis (BA) is analyzing individual behavior. BA can be used to filter useful information from tweets in healthcare and business applications. This paper proposes an analysis of human behavior using Twitter data with the proposed DRDC algorithm. The proposed algorithm uses a multitude of techniques in its pre-processing, feature selection, and classification of tweets. Further, the algorithm’s accuracy is checked using the factors of precision and recall times.
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Behavioral Analysis, Social Media Data Sets, Decision Trees, Document Clustering, Stemming, Pre-processing, DRDC