Twitter sentiment analysis using hyper tuned machine learning models

Twitter sentiment analysis using hyper tuned machine learning models

Registration ID: IJNRD_225259

Published ID: IJNRD2407245

DOI: Click Here to Get

Aakash Saraf

Social media, Machine learning, Classification, Natural Language processing, Support Vector Machine (SVM), Random Forest, Naive Bayes, XGBoost, and Decision Tree

Social media is playing a vital role in communications, and the usage of social media among people has increased dramatically. This growth has paved the way for increased research on sentiment analysis, which helps the individuals and institutions to know about the sentiment on a product, events, topics, politics and more. The research is carried out aiming for sentiment analysis, recommendation systems etc. This paper focuses on sentiment analysis on Twitter posts with the help of machine learning (ML) models. This paper focuses on using different ML models for sentiment analysis. Natural Language processing (NLP) techniques are used in pre-processing by vectorizing the data. In specific, TF-IDF Vectorizer is used. Experimental results showed ML models are more reliable for sentiment analysis. The twitter sentiment classification is performed using algorithms includes Support Vector Machine (SVM), Random Forest, Naive Bayes, XGBoost, and Decision Tree.

Published Paper   E-Certificate

"Twitter sentiment analysis using hyper tuned machine learning models", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 7, page no.c421-c426, July-2024, Available :https://ijnrd.org/papers/IJNRD2407245.pdf

Volume 9 Issue 7, July-2024

Pages : c421-c426

Paper Reg. ID: IJNRD_225259

Published Paper Id: IJNRD2407245

Downloads: 000121157

Research Area: Engineering

Country: Lithia, Florida, United States

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407245.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407245

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

IJNRD is Scholarly open access journals, Peer-reviewed, and Refereed Journals, High Impact factor 8.76 (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) with Open-Access Publications.

INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

Indexing In Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar | AI-Powered Research Tool, Microsoft Academic, Academia.edu, arXiv.org, Research Gate, CiteSeerX, ResearcherID Thomson Reuters, Mendeley : reference manager, DocStoc, ISSUU, Scribd, and many more

How to submit the paper?

Important Dates for Current issue

Paper Submission Open For: August 2025

Current Issue: Volume 10 | Issue 8

Last Date for Paper Submission: Till 31-Aug-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: International Peer-reviewed, Refereed, and Open Access Journal.

Subject Category: Research Area