Sentiment classification of online reviews to travel destinations by supervised machine learning approaches

By Qiang Ye, Ziqiong Zhang, R. Law. Expert systems with applications, 2009

690 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/ed98884e807148fa957cc5ca21b43ae4a5502871

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Alireza Alaei
2. Susanne Becken
3. Bela Stantic
4. Rupali Verma
5. Pansy Nandwani
6. Hanhoon Kang
7. Seong Joon Yoo
8. Dongil Han

In Sociology and Political Science

1. R. Law
2. Stuart J. Barnes
3. Rupali Verma
4. Pansy Nandwani
5. Susanne Becken
6. Alireza Alaei
7. Bela Stantic
8. Qiang Ye

Citations in the corpus

• A review on sentiment analysis and emotion detection from text (2021, methodology): “For instance, Ye et al. (2009) applied sentiment analysis on reviews on European and US destinations labeled on the scale of 1 to 5.”
• Sentiment Analysis in Tourism: Capitalizing on Big Data (2019, methodology): “…(SVM) and Naïve Bayes are the key machine learning methods used for sentiment analysis in the literature (Brob, 2013; Kang et al., 2012; Markopoulos et al., 2015; Shi & Li, 2011; Shimada et al., 2011; Ye et al., 2009), as they were conventionally designed for two-class classification problems.”
• The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings (2011, background): “Previous studies have indicated that the valence of online user-generated reviews may influence sales of books and digital products (Chevlier & Mayzlin, 2006; Ghose & Ipeirotis, 2006; Ye et al., 2009a, 2009b).”
• Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation (2017, background): “For example, most prior tourism studies have principally focused on the impact of online ratings on hotel sales (e.g., Xie et al., 2011; Ye et al., 2009; Ye et al., 2011).”
• Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content (2012, background): “This has led to attempts to extract useful information automatically from the review text (Ye et al. 2009).”
• Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews (2012, methodology): “Similar cases were observed in several sentiment analysis research studies (Dave, Lawrence, & Pennock, 2003; Pang, Lee, & Vaithyanathan, 2002; Ye et al., 2008; Turney, 2002).”
• Sentiment analysis: A review and comparative analysis of web services (2015, methodology): “Regarding the way of assessing this work, and following recent works [101,58], the accuracy, recall, precision and the mean square error have been selected as measures to assess the different capabilities of each tool.”
• The Influence of Destination Image on Tourist Loyalty and Intention to Visit: Testing a Multiple Mediation Approach (2019, background): “Currently, mostly travelers describe their experience through eWOM about destination image, which impacts positively and ultimately leads to enhanced TS [52].”
• The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting (2019, background): “Therangeofapplicationsisindeedveryvast:frauddetection(Aleskerov et al., 1997), online product recommendation (Pazzani & Billsus, 2007; Ye et al., 2009), speech recognition (Hinton et al., 2012), language translation (Cho et al., 2014), image recognition (Krizhevsky et al., 2012), journey planning…”