A machine learning approach to analyze customer satisfaction from airline tweets
Abstract Customer’s experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study presents a machine learning approach to analyze the tweets to improve the customer...
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Online Access: | http://link.springer.com/article/10.1186/s40537-019-0224-1 |
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doaj-539257b28382494ba9463b5eb84ef0c82020-11-25T03:43:05ZengSpringerOpenJournal of Big Data2196-11152019-07-016111610.1186/s40537-019-0224-1A machine learning approach to analyze customer satisfaction from airline tweetsSachin Kumar0Mikhail Zymbler1Department of System Programming, South Ural State UniversityDepartment of System Programming, South Ural State UniversityAbstract Customer’s experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study presents a machine learning approach to analyze the tweets to improve the customer’s experience. Features were extracted from the tweets using word embedding with Glove dictionary approach and n-gram approach. Further, SVM (support vector machine) and several ANN (artificial neural network) architectures were considered to develop classification model that maps the tweet into positive and negative category. Additionally, convolutional neural network (CNN) were developed to classify the tweets and the results were compared with the most accurate model among SVM and several ANN architectures. It was found that CNN outperformed SVM and ANN models. In the end, association rule mining have been performed on different categories of tweets to map the relationship with sentiment categories. The results show that interesting associations were identified that certainly helps the airline industries to improve their customer’s experience.http://link.springer.com/article/10.1186/s40537-019-0224-1TwitterMachine learningConvolutional neural networkAssociation analysisCustomer satisfaction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sachin Kumar Mikhail Zymbler |
spellingShingle |
Sachin Kumar Mikhail Zymbler A machine learning approach to analyze customer satisfaction from airline tweets Journal of Big Data Machine learning Convolutional neural network Association analysis Customer satisfaction |
author_facet |
Sachin Kumar Mikhail Zymbler |
author_sort |
Sachin Kumar |
title |
A machine learning approach to analyze customer satisfaction from airline tweets |
title_short |
A machine learning approach to analyze customer satisfaction from airline tweets |
title_full |
A machine learning approach to analyze customer satisfaction from airline tweets |
title_fullStr |
A machine learning approach to analyze customer satisfaction from airline tweets |
title_full_unstemmed |
A machine learning approach to analyze customer satisfaction from airline tweets |
title_sort |
machine learning approach to analyze customer satisfaction from airline tweets |
publisher |
SpringerOpen |
series |
Journal of Big Data |
issn |
2196-1115 |
publishDate |
2019-07-01 |
description |
Abstract Customer’s experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study presents a machine learning approach to analyze the tweets to improve the customer’s experience. Features were extracted from the tweets using word embedding with Glove dictionary approach and n-gram approach. Further, SVM (support vector machine) and several ANN (artificial neural network) architectures were considered to develop classification model that maps the tweet into positive and negative category. Additionally, convolutional neural network (CNN) were developed to classify the tweets and the results were compared with the most accurate model among SVM and several ANN architectures. It was found that CNN outperformed SVM and ANN models. In the end, association rule mining have been performed on different categories of tweets to map the relationship with sentiment categories. The results show that interesting associations were identified that certainly helps the airline industries to improve their customer’s experience. |
topic |
Twitter Machine learning Convolutional neural network Association analysis Customer satisfaction |
url |
http://link.springer.com/article/10.1186/s40537-019-0224-1 |
work_keys_str_mv |
AT sachinkumar amachinelearningapproachtoanalyzecustomersatisfactionfromairlinetweets AT mikhailzymbler amachinelearningapproachtoanalyzecustomersatisfactionfromairlinetweets AT sachinkumar machinelearningapproachtoanalyzecustomersatisfactionfromairlinetweets AT mikhailzymbler machinelearningapproachtoanalyzecustomersatisfactionfromairlinetweets |
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