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Micro - blogging platforms like Twitter and Wikipedia are increasing main streams which provide user-made information for publishing and sharing messages. Identifying interesting and useful key terms from E-Learning documents are a critical issue in social media because users can suffer with data overload. To resolve the problem we suggest a novel topic model called Latent Dirichlit Allocation (LDA), LDA is an arithmetical and statistical model to discovering the interesting topics that occur in micro-blog. Interesting key words can be found based on the frequency of each word in the input; topics are categorized by using topic modeling technique. LDA works by, first partitioning each document into paragraphs and split into topic of words, Word frequency is a forwarding function acting as an important role in identifying key terms, the interesting key word can be based on frequency. Thus E-learning documents can be simply retrieved and classified using these methods which is also proven by experimental verifications. From the experimental result real world data from twitter shows that our proposed system out performs more than a few other baseline methods.
Keywords:
Micro-blog twitter, topic modeling, LDA, classification and clustering
Cite Article:
"Micro-blog message based interesting key terms Identification", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.3, Issue 12, page no.67 - 74, December-2018, Available :http://www.ijrti.org/papers/IJRTI1812013.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator