Next Article in Journal
Risk Measurement and Risk Modelling Using Applications of Vine Copulas
Next Article in Special Issue
NFC Evaluation in the Development of Mobile Applications for MICE in Tourism
Previous Article in Journal
An Analysis of Decision Factors on the Price of South Korea’s Certified Emission Reductions in Use of Vector Error Correction Model
Previous Article in Special Issue
International Tourism Advertisements on Social Media: Impact of Argument Quality and Source
Open AccessArticle

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

by Gang Ren and Taeho Hong *
College of Business Administration, Pusan National University, Busan 46241, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2017, 9(10), 1765; https://doi.org/10.3390/su9101765
Received: 17 July 2017 / Revised: 22 September 2017 / Accepted: 25 September 2017 / Published: 29 September 2017
(This article belongs to the Special Issue Mobile Technology and Smart Tourism Development)
With the development of Web 2.0, many studies have tried to analyze tourist behavior utilizing user-generated contents. The primary purpose of this study is to propose a topic-based sentiment analysis approach, including a polarity classification and an emotion classification. We use the Latent Dirichlet Allocation model to extract topics from online travel review data and analyze the sentiments and emotions for each topic with our proposed approach. The top frequent words are extracted for each topic from online reviews on Ctrip.com. By comparing the relative importance of each topic, we conclude that many tourists prefer to provide “suggestion” reviews. In particular, we propose a new approach to classify the emotions of online reviews at the topic level utilizing an emotion lexicon, focusing on specific emotions to analyze customer complaints. The results reveal that attraction “management” obtains most complaints. These findings may provide useful insights for the development of attractions and the measurement of online destination image. Our proposed method can be used to analyze reviews from many online platforms and domains. View Full-Text
Keywords: user-generated content; online destination image; latent Dirichlet allocation; tourist attraction; topic-based sentiment analysis; emotion classification user-generated content; online destination image; latent Dirichlet allocation; tourist attraction; topic-based sentiment analysis; emotion classification
Show Figures

Figure 1

MDPI and ACS Style

Ren, G.; Hong, T. Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach. Sustainability 2017, 9, 1765.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop