Sentiment Analysis of Twitter Data Using Machine Learning Techniques

Prof Rajendra Arakh , Mohammad Shaad , Neelesh Gupta , Kuldeep Mishra , Himanshu Kumar

Crisis Management, LSTM, Sentimental Analysis, Tokenization, Vader

In the age of social media, individuals regularly express their thoughts and emotions across various online platforms. Twitter, a prominent microblogging platform, serves as a prime example where users share their perspectives on diverse global events. Sentiment analysis, a crucial aspect of analyzing online discourse, involves discerning the emotional tone of text. This paper explores sentiment analysis on Twitter, employing machine learning and natural language processing techniques to categorize tweets based on their sentiment polarity. Various machine learning algorithms, including Vader, XGBoost, Random Forest, LSTM, and Bidirectional LSTM, are evaluated for their effectiveness in sentiment analysis. The study aims to assess the performance of these models in analyzing sentiments expressed on Twitter, with insights drawn from real-world data. Through sentiment analysis, organizations can gain valuable insights into public opinion, enabling informed decision-making across various domains.

Published Paper   E-Certificate

"Sentiment Analysis of Twitter Data Using Machine Learning Techniques ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 5, page no.563 - 570, May-2024, Available :https://ijsdr.org/papers/IJSDR2405079.pdf

Volume 9 Issue 5, May-2024

Pages : 563 - 570

Paper Reg. ID: IJSDR_211379

Published Paper Id: IJSDR2405079

Downloads: 000347294

Research Area: Science & Technology

Country: Jabalpur , Madhya Pradesh , India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2405079

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2405079

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

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