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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Bibliographic Details
Main Authors: Sachin Kumar, Mikhail Zymbler
Format: Article
Language:English
Published: SpringerOpen 2019-07-01
Series:Journal of Big Data
Subjects:
Online Access:http://link.springer.com/article/10.1186/s40537-019-0224-1
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spelling 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
Twitter
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
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