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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: June 2024

Volume 9 | Issue 6

Impact factor: 8.15

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Paper Title: Sentiment Analysis of Twitter Data Using Machine Learning Techniques
Authors Name: Prof Rajendra Arakh , Mohammad Shaad , Neelesh Gupta , Kuldeep Mishra , Himanshu Kumar
Unique Id: IJSDR2405079
Published In: Volume 9 Issue 5, May-2024
Abstract: 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.
Keywords: Crisis Management, LSTM, Sentimental Analysis, Tokenization, Vader
Cite Article: "Sentiment Analysis of Twitter Data Using Machine Learning Techniques ", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 5, page no.563 - 570, May-2024, Available :http://www.ijsdr.org/papers/IJSDR2405079.pdf
Downloads: 000342234
Publication Details: Published Paper ID: IJSDR2405079
Registration ID:211379
Published In: Volume 9 Issue 5, May-2024
DOI (Digital Object Identifier):
Page No: 563 - 570
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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