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Sentiment and opinion analysis on Twitter about local airlines

Published: 24 November 2017 Publication History

Abstract

Social media allow the users to express and share their information, ideas and opinions through social networking sites. Airline tweets are becoming popular and used as dataset to gauge the concerns of the customers. In this paper, the researchers classified a tweet's polarity through annotation using classifiers Naïve Bayesian, Support Vector Machine and Random Forest to develop a model. The researchers gathered tweets of local airlines concerning their experience in the services provided by local airlines in the Philippines. The researchers also determined the sentiment of a tweet if it is positive, neutral or negative and provided the quantitative and qualitative analyses, as well as opinion analysis, to better understand the result of the experiment.

References

[1]
Tufts, P., Alexa Internet. 2007. Use of web usage trail data to identify relationships between browsable items. U.S. Patent 7,159,023.
[2]
Top Sites in Philippines - Alexa. 2016. Retrieved from http://www.alexa.com/topsites/countries/PH
[3]
Aggarwal, C. C. 2015. Data mining: The textbook. Springer Publishing. IBM T.J. Watson Research Center, Yorktown Heights.
[4]
Pang, B., & Lee, L. 2008. Opinion Mining and Sentiment Analysis. Journal in Foundations and Trends in Information Association for Computational Linguistics (ACL) 2(1--2), 1--135.
[5]
Cambria, E., Schuller, B., Xia, Y. and Havasi, C. 2013. New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), pp.15--21.
[6]
Sahu, S., Rout, S. K., & Mohanty, D. 2015. Twitter Sentiment Analysis - A More Enhanced Way of Classification and Scoring. In Proceedings of the 2015 IEEE International Symposium on Nanoelectronic and Information Systems.
[7]
Mamgain, N., Mehta, E., Mittal, A., & Bhatt, G. 2016. Sentiment analysis of top colleges in India using Twitter data. In Proceedings of the 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT).
[8]
Groot, & R. 2012. Data Mining for Tweet Sentiment Classification: Twitter Sentiment Analysis. Saarbrücken: LAP LAMBERT Academic Publishing.
[9]
Shukri, S. E., Yaghi, R. I., Aljarah, I., & Alsawalqah, H. 2015. Twitter sentiment analysis: A case study in the automotive industry. In Proceedings of the 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT).
[10]
Jain, A. P., & Katkar, V. D. 2015. Sentiments analysis of Twitter data using data mining. 2015 In Proceedings of the International Conference on Information Processing (ICIP).
[11]
Singh, B., & Singh, A. 2015. Strategies For Analyzing Quantitative Data in Research.
[12]
Rambocas, M., & Gama, J. 2013. Marketing Research: The Role of Sentiment Analysis. Universidade do Porto, Faculdade de Economia do Porto.
[13]
Simeon, L. M. 2016. The Philippine Star - Domestic air passengers grow 12% in Q1 - Civil Aeronautics Board: Philippines.
[14]
Medhat, W., Hassan, A., & Korashy, H. 2014. Sentiment analysis algorithms and applications: A survey. A Journal in Shams Engineering, 5 (4), 1093--1113.
[15]
Louppe, G. 2014. Understanding random forests: From theory to practice. arXiv preprint arXiv:1407.7502.
[16]
Wu, X. and Kumar, V. 2009. The top ten algorithm in data mining. International Standard Book. In Proceedings of the IEEE International Conference on Data Mining. ICDM '06 in Hong Kong. 13, pp.978--1.
[17]
Oelke, D., Hao, M., Rohrdantz, C., Keim, D. A., Dayal, U., Haug, L., & Janetzko, H. 2009. Visual opinion analysis of customer feedback data. In Proceedings of the 2009 IEEE Symposium on Visual Analytics Science and Technology.
[18]
Hajmohammadi, M. 2012. Opinion Mining and Sentiment Analysis: A Survey. 2012 International Journal of Computers & Technology (IJCT).
[19]
Narr, S., Hulfenhaus, M. and Albayrak, S. 2012. Language-independent twitter sentiment analysis. Knowledge discovery and machine learning (KDML), LWA, pp.12--14.

Cited By

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  • (2022)A Semi-Supervised Approach to Sentiment Analysis of Tweets during the 2022 Philippine Presidential ElectionInformation10.3390/info1310048413:10(484)Online publication date: 9-Oct-2022
  • (2022)Sentiment Analysis using Ensemble Technique on Textual and Emoticon Data2022 International Conference on Innovations in Science, Engineering and Technology (ICISET)10.1109/ICISET54810.2022.9775836(255-259)Online publication date: 26-Feb-2022
  • (2022)VaderLogRest Algorithm: An Ensemble Learning Approach for Sentiment Analysis on Vaccination Tweets2022 4th International Conference on Biomedical Engineering (IBIOMED)10.1109/IBIOMED56408.2022.9988439(7-12)Online publication date: 18-Oct-2022
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    cover image ACM Other conferences
    ICCIP '17: Proceedings of the 3rd International Conference on Communication and Information Processing
    November 2017
    545 pages
    ISBN:9781450353656
    DOI:10.1145/3162957
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 24 November 2017

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    Author Tags

    1. airline
    2. opinion analysis
    3. qualitative analysis
    4. quantitative analysis
    5. sentiment analysis

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    Overall Acceptance Rate 61 of 301 submissions, 20%

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    Cited By

    View all
    • (2022)A Semi-Supervised Approach to Sentiment Analysis of Tweets during the 2022 Philippine Presidential ElectionInformation10.3390/info1310048413:10(484)Online publication date: 9-Oct-2022
    • (2022)Sentiment Analysis using Ensemble Technique on Textual and Emoticon Data2022 International Conference on Innovations in Science, Engineering and Technology (ICISET)10.1109/ICISET54810.2022.9775836(255-259)Online publication date: 26-Feb-2022
    • (2022)VaderLogRest Algorithm: An Ensemble Learning Approach for Sentiment Analysis on Vaccination Tweets2022 4th International Conference on Biomedical Engineering (IBIOMED)10.1109/IBIOMED56408.2022.9988439(7-12)Online publication date: 18-Oct-2022
    • (2021)Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve BayesInformation10.3390/info1205020412:5(204)Online publication date: 11-May-2021
    • (2019)The complexity of comments from Senegalese online presses face with opinion mining methods2019 14th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI.2019.8760874(1-6)Online publication date: Jun-2019
    • (2018)Sentence-Level Sarcasm Detection in English and Filipino TweetsProceedings of the 4th International Conference on Industrial and Business Engineering10.1145/3288155.3288172(181-186)Online publication date: 24-Oct-2018

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