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IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

Issue: March 2024

Volume 9 | Issue 3

Impact factor: 8.15

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Paper Title: Twitter Sentiment Analysis Using Ordinal Regression
Authors Name: Y Manoj Kumar , Y Vishnu Vardhan , Y Satya Sai , Y Vamshi Vardhan Reddy , M Revathi
Unique Id: IJSDR2304101
Published In: Volume 8 Issue 4, April-2023
Abstract: In recent years, research on Twitter sentiment analysis, which analyzes Twitter data(tweets) to extract user sentiments about a topic, has grown rapidly. Many researchers prefer the use of machine learning algorithms for such analysis. This study aims to perform a detailed sentiment analysis of tweets based on ordinal regression using machine learning techniques. The proposed approach consists of first pre-processing tweets and using a feature extraction method that creates an efficient feature. Then, under several classes, these features scoring and balancing. Multinomial logistic regression and Naive Bayes algorithms are used for sentiment analysis classification in the proposed framework. For the actual implementation of this system, a twitter dataset publicly made available by the NLTK corpora resources is used. Experimental findings reveal that the proposed approach can detect ordinal regression using machine learning methods with good accuracy.
Keywords: Machine learning, Multinomial Logistic, Naïve Bayes, Data Collection, Data set.
Cite Article: "Twitter Sentiment Analysis Using Ordinal Regression", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 4, page no.544 - 548, April-2023, Available :http://www.ijsdr.org/papers/IJSDR2304101.pdf
Downloads: 000336256
Publication Details: Published Paper ID: IJSDR2304101
Registration ID:205024
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 544 - 548
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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