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

Aakash Saraf (July-2024). Twitter sentiment analysis using hyper tuned machine learning models. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), c421-c426. 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: 000122016

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

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