Call for Papers:Vol.11 Issue.3

Submission
Last date		 28-Jun-2025
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-3. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  Exploring Sentiment Trends on Twitter: A Machine Learning Approach for Analyzing Public Opinion
PaperId: :  22811
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-22811
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Darshan Ravindra PatilGovernment Polytechnic Jalgaon
Vaibhav Ramachandra ChavanGovernment Polytechnic Jalgaon

Abstract

Information Technology
sentiment analysis; text classification; natural language processing; Twitter
With the exponential growth of web technology, the volume of data on the internet has reached unprecedented levels. The internet has evolved into a platform for online learning, idea exchange, and opinion sharing. Social networking sites such as Twitter, Facebook, and Google+ have gained immense popularity, enabling users to share views, engage in discussions, and post messages globally. This survey focuses on sentiment analysis of Twitter data, which is crucial for analyzing opinions expressed in tweets, known for their unstructured and heterogeneous nature. We provide an overview and comparative analysis of existing sentiment analysis techniques, including machine learning and lexicon-based approaches, along with evaluation metrics. We explore the use of machine learning algorithms such as Naive Bayes, Maximum Entropy, and Support Vector Machine for sentiment analysis of Twitter data streams. Additionally, we discuss the general challenges and applications of sentiment analysis on Twitter.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE Darshan Ravindra Patil, and Vaibhav Ramachandra Chavan. "Exploring Sentiment Trends on Twitter: A Machine Learning Approach for Analyzing Public Opinion" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 2864-2871
MLA Darshan Ravindra Patil, and Vaibhav Ramachandra Chavan. "Exploring Sentiment Trends on Twitter: A Machine Learning Approach for Analyzing Public Opinion." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 2864-2871.
APA Darshan Ravindra Patil, & Vaibhav Ramachandra Chavan. (2024). Exploring Sentiment Trends on Twitter: A Machine Learning Approach for Analyzing Public Opinion. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 2864-2871.
Chicago Darshan Ravindra Patil, and Vaibhav Ramachandra Chavan. "Exploring Sentiment Trends on Twitter: A Machine Learning Approach for Analyzing Public Opinion." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 2864-2871.
Oxford Darshan Ravindra Patil, and Vaibhav Ramachandra Chavan. 'Exploring Sentiment Trends on Twitter: A Machine Learning Approach for Analyzing Public Opinion', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 2864-2871. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Exploring_Sentiment_Trends_on_Twitter__A_Machine_Learning_Approach_for_Analyzing Public Opinion_ijariie22811.pdf (Accessed : 22 March 2025).
Harvard Darshan Ravindra Patil, and Vaibhav Ramachandra Chavan. (2024) 'Exploring Sentiment Trends on Twitter: A Machine Learning Approach for Analyzing Public Opinion', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 2864-2871IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Exploring_Sentiment_Trends_on_Twitter__A_Machine_Learning_Approach_for_Analyzing Public Opinion_ijariie22811.pdf (Accessed : 22 March 2025)
IEEE Darshan Ravindra Patil, and Vaibhav Ramachandra Chavan, "Exploring Sentiment Trends on Twitter: A Machine Learning Approach for Analyzing Public Opinion," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 2864-2871, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Exploring_Sentiment_Trends_on_Twitter__A_Machine_Learning_Approach_for_Analyzing Public Opinion_ijariie22811.pdf [Accessed : 22 March 2025].
Turabian Darshan Ravindra Patil, and Vaibhav Ramachandra Chavan. "Exploring Sentiment Trends on Twitter: A Machine Learning Approach for Analyzing Public Opinion." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 (22 March 2025).
Vancouver Darshan Ravindra Patil, and Vaibhav Ramachandra Chavan. Exploring Sentiment Trends on Twitter: A Machine Learning Approach for Analyzing Public Opinion. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : 22 March 2025]; 10(2) : 2864-2871. Available from: https://ijariie.com/AdminUploadPdf/Exploring_Sentiment_Trends_on_Twitter__A_Machine_Learning_Approach_for_Analyzing Public Opinion_ijariie22811.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads


Last download on 3/22/2025 7:43:14 PM

Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
The Farmer PortalInformation TechnologySakshi Yede Download
DYNAMIC MULTI-KEY AUTHENTICATION FOR IOT SYSTEM USING SECURE VAULTSInformation Science EngineeringVinay M G Download
Eye Glaucoma Detection Using Machine LearningInformation Science & Engineering Uday M B Download
The Shift to Electronic Payment Methods in Commercial Banks: Benefits and ChallengesDigital BankingEssam Zaid Shoaib Al-Dabbashi Download
DEEP ADAPTIVE FEATURE FUSION FOR ORIGIN DESTINATION PASSENGER FLOW FORECASTING IN MASS EVENTSInformation TechnologyBHUKYA RAJAKUMAR Download
REAL TIME EMERGENCY VEHICLE DETECTION USING MACHINE LEARNINGComputer Science and EngineeringAravinthkumar A Download
COLLEGIUMBOT: AN AI-DRIVEN CHATBOT FOR ENHANCING STUDENT ENGAGEMENT AND CAMPUS LIFE THROUGH VOICE-BASED INTERACTIONInformation TechnologySIVASANKAR CHITTOOR Download
AUTOMATED VIDEO TEXT EXTRACTION AND SUMMARIZATION SYSTEM USING LSTM NETWORKSInformation TechnologyT SUNDARARAJULU Download
BLOOD GROUP DETECTION USING FINGERPRINTInformation TechnologyN ANITHA Download
SOLAR RADIATION PREDICTION USING MACHINE LEARNINGInformation TechnologyM RAM KUMAR Download
DEFENDING AGAINST POISONING ATTACKS IN FEDERATED LEARNING WITH BLOCKCHAININFORMATION TECHNOLOGYT.SUNDARARAJULU. Download
DEEP CONVOLUTIONAL NEURAL NETWORK FOR ROBUST DETECTION OF OBJECT-BASED FORGERIES IN ADVANCED VIDEOINFORMATION TECHNOLOGYD.VISWASAHITYA Download
SMART CAR PRICE PREDICTOR USING MACHINE LEARNINGINFORMATION TECHNOLOGYS.JASMIN Download
SUPERVISED LEARNING MODEL INSIGHTS AND EVALUATIONInformation TechnologyB. RAJA KUMAR Download
Enhancing Forensic Data Protection: A Robust Storage Framework with Verified Access and Intelligent Key-Based EncryptionComputer Science EngineeringN ANITHA Download
12