Reference Hub

This research has been cited in:

Chapter
Sentiment Analysis of Student Evaluations of Teaching Using Deep Learning ApproachInternational Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020)10.1007/978-3-030-80216-5_20
Article
Gauging indirect stakeholder sentiment towards orphanage tourism on TwitterTourism Recreation Research10.1080/02508281.2022.2157546
Article
Proposal of a Method for the Analysis of Sentiments in Social Networks with the Use of RInformatics10.3390/informatics9030063
Article
The morality and tax avoidance: A sentiment and position taking analysisPLOS ONE10.1371/journal.pone.0287327
Article
Impact of COVID-19 pandemic on ride-hailing services based on large-scale Twitter data analysisJournal of Urban Management10.1016/j.jum.2021.03.002
Article
Sentiment analysis on images using different transfer learning modelsProcedia Computer Science10.1016/j.procs.2023.01.142
Article
Twitter sentiment analysis for stock predictionSSRN Electronic Journal10.2139/ssrn.4157658
Article
Topic Modelling and Opinion Analysis On Climate Change Twitter Data Using LDA And BERT Model.Procedia Computer Science10.1016/j.procs.2023.01.071
Article
Sentiment analysis and classification of Indian farmers’ protest using twitter dataInternational Journal of Information Management Data Insights10.1016/j.jjimei.2021.100019
Article
Sentiment Analysis of Omicron Tweets by using Machine Learning ModelsVFAST Transactions on Software Engineering10.21015/vtse.v11i1.1450
Chapter
Sentiment Analysis on Public Transportation During Covid-19: An Exploratory StudyIndustrial Engineering in the Age of Business Intelligence10.1007/978-3-031-08782-0_10
Chapter
Analyzing Comments on Social Media with XG Boost MechanismMachine Intelligence and Soft Computing10.1007/978-981-16-8364-0_5
Article
Local food experiences before and after COVID-19: a sentiment analysis of EWOMTourism and hospitality management10.20867/thm.29.4.1
Article
Job satisfaction, management sentiment, and financial performance: Text analysis with job reviews from indeed.comInternational Journal of Information Management Data Insights10.1016/j.jjimei.2023.100155
Chapter
Cyberbullying Awareness Through Sentiment Analysis Based on TwitterKids Cybersecurity Using Computational Intelligence Techniques10.1007/978-3-031-21199-7_14
Article
A Mixed Malay–English Language COVID-19 Twitter Dataset: A Sentiment AnalysisBig Data and Cognitive Computing10.3390/bdcc7020061
Article
A systematic review of social network sentiment analysis with comparative study of ensemble-based techniquesArtificial Intelligence Review10.1007/s10462-023-10472-w
Article
Studying public perception about covaxin vaccination: A sentiment analysis of tweetsJournal of Public Health and Primary Care10.4103/jphpc.jphpc_13_21
Conference
Corpus Usage for Sentiment Analysis of a Hashtag Twitter2019 Fourth International Conference on Informatics and Computing (ICIC)10.1109/ICIC47613.2019.8985772
Chapter
Sentiment Analysis of Political Tweets for Israel Using Machine LearningMachine Learning and Big Data Analytics10.1007/978-3-031-15175-0_15
Article
A text mining analysis of the change in status of the Hagia Sophia on Twitter: the political discourse and its reflections on the public opinionAtlantic Journal of Communication10.1080/15456870.2022.2093354
Article
An Improvised Sentiment Analysis Model on Twitter Data Using Stochastic Gradient Descent (SGD) Optimization Algorithm in Stochastic Gate Neural Network (SGNN)SN Computer Science10.1007/s42979-022-01607-x
Article
The tinder swindler: Analyzing public sentiments of romance fraud using machine learning and artificial intelligenceJournal of Economic Criminology10.1016/j.jeconc.2023.100023
Conference
Machine Learning Approach for Sentiment Analysis in Crime Information Retrieval2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)10.1109/IC2IE50715.2020.9274607
Conference
Sentiment Analysis on Online Reviews using Machine Learning and NLTK2022 6th International Conference on Trends in Electronics and Informatics (ICOEI)10.1109/ICOEI53556.2022.9776850
Article
Tracking How a Change in a Telecom Service Affects Its Customers Using Sentiment Analysis and Personality InsightInternational Journal of Service Science, Management, Engineering, and Technology10.4018/IJSSMET.2020070103
Chapter
Predicting Catastrophic Events Using Machine Learning Models for Natural Language ProcessingData Mining Approaches for Big Data and Sentiment Analysis in Social Media10.4018/978-1-7998-8413-2.ch010
Chapter
Tracking How a Change in a Telecom Service Affects Its Customers Using Sentiment Analysis and Personality InsightResearch Anthology on Implementing Sentiment Analysis Across Multiple Disciplines10.4018/978-1-6684-6303-1.ch092
Article
Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning TechniquesInternational Journal of Service Science, Management, Engineering, and Technology10.4018/IJSSMET.298669
Article
Sentiment Classification of Social Network Text Based on AT-BiLSTM Model in a Big Data EnvironmentInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.324808
Article
Implementation of Text Mining in Socio-Economic ResearchInternational Journal of Business Data Communications and Networking10.4018/IJBDCN.341263

Sentiment Analysis of Twitter Data: A Hybrid Approach

Ankit Srivastava, Vijendra Singh, Gurdeep Singh Drall

Source Title: International Journal of Healthcare Information Systems and Informatics (IJHISI)14(2)

ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781522564539|DOI: 10.4018/IJHISI.2019040101

Cite Article Cite Article

MLA

Srivastava, Ankit, et al. "Sentiment Analysis of Twitter Data: A Hybrid Approach." IJHISI vol.14, no.2 2019: pp.1-16. http://doi.org/10.4018/IJHISI.2019040101

APA

Srivastava, A., Singh, V., & Drall, G. S. (2019). Sentiment Analysis of Twitter Data: A Hybrid Approach. International Journal of Healthcare Information Systems and Informatics (IJHISI), 14(2), 1-16. http://doi.org/10.4018/IJHISI.2019040101

Chicago

Srivastava, Ankit, Vijendra Singh, and Gurdeep Singh Drall. "Sentiment Analysis of Twitter Data: A Hybrid Approach," International Journal of Healthcare Information Systems and Informatics (IJHISI) 14, no.2: 1-16. http://doi.org/10.4018/IJHISI.2019040101

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

Over the past few years, the novel appeal and increasing popularity of social networks as a medium for users to express their opinions and views have created an accumulation of a massive amount of data. This evolving mountain of data is commonly termed Big Data. Accordingly, one area in which the application of new techniques in data mining research has significant potential to achieve more precise classification of hidden knowledge in Big Data is sentiment analysis (aka optimal mining). A hybrid approach using Naïve Bayes and Random Forest on mining Twitter datasets is presented here as an extension of previous work. Briefly, relevant data sets are collected from Twitter using Twitter API; then, use of the hybrid methodology is illustrated and evaluated against one with only Naïve Bayes classifier. Results show better accuracy and efficiency in the sentiment classification for the hybrid approach.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

Sentiment Analysis of Twitter Data: A Hybrid Approach

MLA

APA

Chicago

Export Reference

Abstract

Request Access