Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/75670
Title: Significant labels in sentiment analysis of online customer reviews of airlines
Authors: Mohamed Zaki Ahmed, Ayat 
Rodríguez Díaz, Manuel 
UNESCO Clasification: 531212 Transportes y comunicaciones
Keywords: Airline
Content Analysis
Key Label
Machine Learning
Online Customer Review, et al
Issue Date: 2020
Journal: Sustainability (Switzerland) 
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.
URI: http://hdl.handle.net/10553/75670
DOI: 10.3390/su12208683
Source: Sustainability (Switzerland)[EISSN 2071-1050],v. 12 (20), p. 1-18, (Octubre 2020)
Appears in Collections:Artículos
Thumbnail
Adobe PDF (403,9 kB)
Show full item record

SCOPUSTM   
Citations

4
checked on Jun 26, 2022

Page view(s)

122
checked on May 21, 2022

Download(s)

97
checked on May 21, 2022

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.