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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

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Impact Factor : 8.14

Issue per Year : 12

Volume Published : 8

Issue Published : 85

Article Submitted : 7821

Article Published : 4002

Total Authors : 10443

Total Reviewer : 547

Total Countries : 81

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Published Paper Id: IJRTI1904016
Published In: Volume 4 Issue 4, April-2019
Abstract: Techniques for learning choice standards are as effectively connected to numerous issue areas, specifically when comprehension and translation of the educated model are vital many problem domains, in particular when understanding and interpretation of the learned model is necessary. In numerous genuine issues, might want to anticipate differently related (ostensible or numeric) target properties at the same time. While a few techniques for learning decides that foresee numerous objectives on the double exist. In the most widely recognized machine picking up setting, one predicts the estimation of a single target quality, straight out or numeric. Characteristic speculation of this setting is to anticipate multiple target qualities all the while. The undertaking comes in two marginally extraordinary flavors. . In multi-target expectation, all objective characteristics are (similarly) critical and anticipated at the same time with a solitary model. Perform multiple tasks learning then again, initially centered around a solitary target quality and utilized the rest for help as it were. These days, be that as it may, perform multiple tasks models ordinarily anticipate each objective property separately however within any event somewhat particular models. In this undertaking, we pick the Twitter application for multivalve expectation. Twitter is one of online networking with in excess of 500 million clients and 400 million tweets for every day. In any composed twits of twitter clients, it contains a different feeling. The vast majority of the examination on the utilization of web-based life conclusion investigation classifications into three positive, negative and neutral. In this task, to identify the feeling of Twitter clients that are characterized into 6 classes, specifically joy, miserable, furious, shock, appall and dread.
Keywords: Multi value prediction, Text mining, Naïve Bayes
Cite Article: "MULTIVALUE PREDICTION IN TWITTER USING NAIVE BAYES ALGORITHM", International Journal of Science & Engineering Development Research (, ISSN:2455-2631, Vol.4, Issue 4, page no.73 - 76, April-2019, Available :
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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
Publication Details: Published Paper ID: IJRTI1904016
Registration ID:180761
Published In: Volume 4 Issue 4, April-2019
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Page No: 73 - 76
Country: Kottayam, Kerala, India
Research Area: Engineering
Publisher : IJ Publication
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

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