UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 11 Issue 1
January-2024
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2401635


Registration ID:
531864

Page Number

g309-g313

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Title

From Tweets to Trends: NLP -Driven Social Media Sentiment Analysis

Abstract

Natural Language Processing (NLP) is a disciplined of artificial intelligence that aims to understand and interpret human language in context. With the rise of social media, NLP can be used in social media analytics to enhance decision-making, analyses user sentiments, speed up data processing, and gain deeper knowledge of user behavior. This paper explores the application of NLP techniques in analyzing social media content to uncover trends, sentiment, and patterns.

Key Words

Natural Language Processing, sentiment analysis, social media analytics

Cite This Article

"From Tweets to Trends: NLP -Driven Social Media Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.g309-g313, January-2024, Available :http://www.jetir.org/papers/JETIR2401635.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"From Tweets to Trends: NLP -Driven Social Media Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppg309-g313, January-2024, Available at : http://www.jetir.org/papers/JETIR2401635.pdf

Publication Details

Published Paper ID: JETIR2401635
Registration ID: 531864
Published In: Volume 11 | Issue 1 | Year January-2024
DOI (Digital Object Identifier):
Page No: g309-g313
Country: Mumbai, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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