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Machine Learning based Airlines Tweets Sentiment Classification
by
Akshada Sunil Shitole,
Archana Suhas Vaidya
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 20 |
| Year of Publication: 2023 |
| Authors: Akshada Sunil Shitole, Archana Suhas Vaidya |
10.5120/ijca2023922922
|
Akshada Sunil Shitole, Archana Suhas Vaidya . Machine Learning based Airlines Tweets Sentiment Classification. International Journal of Computer Applications. 185, 20 ( Jul 2023), 32-35. DOI=10.5120/ijca2023922922
@article{
10.5120/ijca2023922922,
author = {
Akshada Sunil Shitole,
Archana Suhas Vaidya
},
title = { Machine Learning based Airlines Tweets Sentiment Classification },
journal = {
International Journal of Computer Applications
},
issue_date = { Jul 2023 },
volume = { 185 },
number = { 20 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = {
32-35
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume185/number20/32810-2023922922/
},
doi = { 10.5120/ijca2023922922 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:36.261926+05:30
%A Akshada Sunil Shitole
%A Archana Suhas Vaidya
%T Machine Learning based Airlines Tweets Sentiment Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 20
%P 32-35
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Sentiment Analysis is one of the key research areas under the machine learning. In this research, the sentiment analysis is applied on the tweets which are based on airlines services. Sentiment analysis is done to classify the sentiments into either positive or negative. Various supervised and unsupervised machine learning algorithms are applied and their accuracy scores are estimated. Based on the accuracy score estimation the best machine learning algorithm for sentiment analysis is identified. The Experiment is carried out with the help of 14640 Airlines related tweets. Support Vector Machine algorithm shows the highest performance accuracy results of 90% and the lowest accuracy result of 79% is given by Decision tree machine learning algorithm. The result shows Support Vector machine algorithm performs better for the sentiment analysis of airlines tweets.
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Index Terms
Computer Science
Information Sciences
Keywords