|
Airline passenger’s sentiment analysis for improving the quality of airline services by using a deep learning approach
|
A. Nourbakhsh   , M. Rezaei Chelkasari |
| Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran |
|
|
Abstract: (807 Views) |
| Advances in technology have increased the availability and use of smartphones. Customer experience is one of the major concerns in the aviation industry. Twitter is one of the most popular social media platforms where travelers can share their feedback. Tweets' Classification based on user sentiments, is an important and common task which has addressed in many researches. Data mining, text mining, web mining, classification for analysis, and illustrating Twitter comments are some of the activities carried out in this field. Text mining is one of the prominent fields of data mining that able to extract useful information from travelers' tweets. This study presents a machine learning-based method for tweets analyzing to improve customer experience handling. The deep learning algorithm identifies ambiguous tweets and decides based on the level of ambiguity. The proposed method provides the feature vector for classification by extracting the word vector from the text analysis, constructing the added Message polarity feature with the WordNet dictionary from the trained examples. The results obtained from the deep learning algorithm validation show that the proposed method is able to identify passenger sentiments in two-class analysis with 99.97% accuracy and in a three-class analysis with 88.83% accuracy. |
|
| Keywords: Deep Learning, Opinion Mining, WordNet Dictionary, Tweet Sentiments Analysis, Machine Learning |
|
|
Full-Text [PDF 952 kb]
(737 Downloads)
|
Type of Study: Applicable |
Subject:
General
Received: 2022/02/9 | Accepted: 2022/07/2
|
|
|
|
|
|
|
| Send email to the article author |
|
|
|
| Add your comments about this article |
|
|
|
