Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines

Ayat Zaki Ahmed and Manuel Rodríguez-Díaz
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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

Sustainability, 2020, vol. 12, issue 20, 1-18

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.

Keywords: sentiment analysis; content analysis; online customer review; airline; key label; machine learning; social media (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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