IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i20p8683-d431551.html
   My bibliography  Save this article

Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines

Author

Listed:
  • Ayat Zaki Ahmed

    (Department of Economics and Business, Faculty of Economy, Business and Tourism, University of Las Palmas de Gran Canaria, 35001 Las Palmas, Spain)

  • Manuel Rodríguez-Díaz

    (Department of Economics and Business, Faculty of Economy, Business and Tourism, University of Las Palmas de Gran Canaria, 35001 Las Palmas, Spain)

Abstract

Sentiment analysis is becoming an essential tool for analyzing the contents of online customer reviews. This analysis involves identifying the necessary labels to determine whether a comment is positive, negative, or neutral, and the intensity with which the customer’s sentiment is expressed. Based on this information, service companies such as airlines can design and implement a communication strategy to improve their customers’ image of the company and the service received. This study proposes a methodology to identify the significant labels that represent the customers’ sentiments, based on a quantitative variable, that is, the overall rating. The key labels were identified in the comments’ titles, which usually include the words that best define the customer experience. This database was applied to more extensive online customer reviews in order to validate that the identified tags are meaningful for assessing the sentiments expressed in them. The results show that the labels elaborated from the titles are valid for analyzing the feelings in the comments, thus, simplifying the labels to be taken into account when carrying out a sentiment analysis of customers’ online comments.

Suggested Citation

  • Ayat Zaki Ahmed & Manuel Rodríguez-Díaz, 2020. "Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines," Sustainability, MDPI, vol. 12(20), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8683-:d:431551
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/20/8683/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/20/8683/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Claes Fornell & Sunil Mithas & Forrest V. Morgeson, III, 2009. "—The Economic and Statistical Significance of Stock Returns on Customer Satisfaction," Marketing Science, INFORMS, vol. 28(5), pages 820-825, 09-10.
    2. Yang Liu & Jian-Wu Bi & Zhi-Ping Fan, 2017. "A Method for Ranking Products Through Online Reviews Based on Sentiment Classification and Interval-Valued Intuitionistic Fuzzy TOPSIS," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1497-1522, November.
    3. Farooq, Muhammad Shoaib & Salam, Maimoona & Fayolle, Alain & Jaafar, Norizan & Ayupp, Kartinah, 2018. "Impact of service quality on customer satisfaction in Malaysia airlines: A PLS-SEM approach," Journal of Air Transport Management, Elsevier, vol. 67(C), pages 169-180.
    4. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    5. Maria Grazia Bellizzi & Luigi dell’Olio & Laura Eboli & Carmen Forciniti & Gabriella Mazzulla, 2020. "Passengers’ Expectations on Airlines’ Services: Design of a Stated Preference Survey and Preliminary Outcomes," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
    6. Michael Anderson & Jeremy Magruder, 2012. "Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database," Economic Journal, Royal Economic Society, vol. 122(563), pages 957-989, September.
    7. Hussain, Rahim & Al Nasser, Amjad & Hussain, Yomna K., 2015. "Service quality and customer satisfaction of a UAE-based airline: An empirical investigation," Journal of Air Transport Management, Elsevier, vol. 42(C), pages 167-175.
    8. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    9. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    10. Christopher Ittner & David Larcker & Daniel Taylor, 2009. "—The Stock Market's Pricing of Customer Satisfaction," Marketing Science, INFORMS, vol. 28(5), pages 826-835, 09-10.
    11. Tahanisaz, Sahar & shokuhyar, Sajjad, 2020. "Evaluation of passenger satisfaction with service quality: A consecutive method applied to the airline industry," Journal of Air Transport Management, Elsevier, vol. 83(C).
    12. Fang, Bin & Ye, Qiang & Kucukusta, Deniz & Law, Rob, 2016. "Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics," Tourism Management, Elsevier, vol. 52(C), pages 498-506.
    13. Sezgen, Eren & Mason, Keith J. & Mayer, Robert, 2019. "Voice of airline passenger: A text mining approach to understand customer satisfaction," Journal of Air Transport Management, Elsevier, vol. 77(C), pages 65-74.
    14. Lee, Kiljae & Yu, Chunyan, 2018. "Assessment of airport service quality: A complementary approach to measure perceived service quality based on Google reviews," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 28-44.
    15. Gerard J. Tellis & Joseph Johnson, 2007. "The Value of Quality," Marketing Science, INFORMS, vol. 26(6), pages 758-773, 11-12.
    16. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    17. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    18. West, Patricia M & Broniarczyk, Susan M, 1998. "Integrating Multiple Opinions: The Role of Aspiration Level on Consumer Response to Critic Consensus," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(1), pages 38-51, June.
    19. Ayat Zaki Ahmed & Manuel Rodríguez-Díaz, 2020. "Analyzing the Online Reputation and Positioning of Airlines," Sustainability, MDPI, vol. 12(3), pages 1-27, February.
    20. Manuel Rodríguez-Díaz & Crina Isabel Rodríguez-Voltes & Ana Cristina Rodríguez-Voltes, 2018. "Gap Analysis of the Online Reputation," Sustainability, MDPI, vol. 10(5), pages 1-15, May.
    21. O'Sullivan, Don & Hutchinson, Mark C. & O'Connell, Vincent, 2009. "Empirical evidence of the stock market's (mis)pricing of customer satisfaction," International Journal of Research in Marketing, Elsevier, vol. 26(2), pages 154-161.
    22. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
    23. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Laith T. Khrais, 2020. "Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce," Future Internet, MDPI, vol. 12(12), pages 1-14, December.
    2. Devika Rani Sharma & Smitha Girija & Pratima Merugu, 2022. "Mediating Role of Perceived Health Risk on Customer Experience and Customer Satisfaction: Evidence from the Airline Industry in India During COVID-19," International Journal of Global Business and Competitiveness, Springer, vol. 17(1), pages 31-45, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    2. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    3. Ayat Zaki Ahmed & Manuel Rodríguez Díaz, 2022. "A Methodology for Machine-Learning Content Analysis to Define the Key Labels in the Titles of Online Customer Reviews with the Rating Evaluation," Sustainability, MDPI, vol. 14(15), pages 1-31, July.
    4. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    5. Xitong Li, 2018. "Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold," Information Systems Research, INFORMS, vol. 29(3), pages 739-754, September.
    6. Kick, Markus, 2015. "Social Media Research: A Narrative Review," EconStor Preprints 182506, ZBW - Leibniz Information Centre for Economics.
    7. Bellizzi, Maria Grazia & Eboli, Laura & Mazzulla, Gabriella & Postorino, Maria Nadia, 2022. "Classification trees for analysing highly educated people satisfaction with airlines’ services," Transport Policy, Elsevier, vol. 116(C), pages 199-211.
    8. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    9. Angela Aerry Choi & Daegon Cho & Dobin Yim & Jae Yun Moon & Wonseok Oh, 2019. "When Seeing Helps Believing: The Interactive Effects of Previews and Reviews on E-Book Purchases," Information Systems Research, INFORMS, vol. 30(4), pages 1164-1183, December.
    10. Wang, Feng & Liu, Xuefeng & Fang, Eric (Er), 2015. "User Reviews Variance, Critic Reviews Variance, and Product Sales: An Exploration of Customer Breadth and Depth Effects," Journal of Retailing, Elsevier, vol. 91(3), pages 372-389.
    11. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    12. Pei-Yu Chen & Yili Hong & Ying Liu, 2018. "The Value of Multidimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments," Management Science, INFORMS, vol. 64(10), pages 4629-4647, October.
    13. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    14. Hyunwoo Hwangbo & Jonghyuk Kim, 2019. "A Text Mining Approach for Sustainable Performance in the Film Industry," Sustainability, MDPI, vol. 11(11), pages 1-16, June.
    15. Seshadri Tirunillai & Gerard J. Tellis, 2012. "Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," Marketing Science, INFORMS, vol. 31(2), pages 198-215, March.
    16. Cheng Zhao & Chong Alex Wang, 2023. "A cross-site comparison of online review manipulation using Benford’s law," Electronic Commerce Research, Springer, vol. 23(1), pages 365-406, March.
    17. Marchand, André & Hennig-Thurau, Thorsten & Wiertz, Caroline, 2017. "Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 336-354.
    18. Supriyo Mandal & Abyayananda Maiti, 2022. "Network promoter score (NePS): An indicator of product sales in E-commerce retailing sector," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1327-1349, September.
    19. Floyd, Kristopher & Freling, Ryan & Alhoqail, Saad & Cho, Hyun Young & Freling, Traci, 2014. "How Online Product Reviews Affect Retail Sales: A Meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 217-232.
    20. Young Kwark & Gene Moo Lee & Paul A. Pavlou & Liangfei Qiu, 2021. "On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data," Information Systems Research, INFORMS, vol. 32(3), pages 895-913, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8683-:d:431551. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.