Sentiment Analysis of Hotel Reviews in Greek: A Comparison of Unigram Features

Markopoulos, George; Mikros, George K.; Iliadi, Anastasia; Liontos, Michalis

doi:10.1007/978-3-319-15859-4_31

Cited by 21 publications

(23 citation statements)

References 9 publications

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“…The trained classifier is finally used to determine the polarity of new text. Support vector machine (SVM) and Naïve Bayes are the key machine learning methods used for sentiment analysis in the literature (Brob 2013; Kang, Yoo, and Han 2012; Markopoulos et al 2015; Shi and Li 2011; Shimada et al 2011; Ye, Zhang, and Law 2009), as they were conventionally designed for two-class classification problems. A SVM is a classifier which uses annotated data for training to obtain an optimal separating hyperplane/line to accurately categorize new samples data into different groups.…”

Section: What Is Sentiment Analysis?mentioning

confidence: 99%

Sentiment Analysis in Tourism: Capitalizing on Big Data

Alaei

Becken

Stantić

2017

Journal of Travel Research

457

266

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Advances in technology have fundamentally changed how information is produced and consumed by all actors involved in tourism. Tourists can now access different sources of information, and they can generate their own content and share their views and experiences. Tourism content shared through social media has become a very influential information source that impacts tourism in terms of both reputation and performance. However, the volume of data on the Internet has reached a level that makes manual processing almost impossible, demanding new analytical approaches. Sentiment analysis is rapidly emerging as an automated process of examining semantic relationships and meaning in reviews. In this article, different sentiment analysis approaches applied in tourism are reviewed and assessed in terms of the datasets used and performances on key evaluation metrics. The article concludes by outlining future research avenues to further advance sentiment analysis in tourism as part of a broader Big Data approach.

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Section: What Is Sentiment Analysis?mentioning

confidence: 99%

Sentiment Analysis in Tourism: Capitalizing on Big Data

Alaei

Becken

Stantić

2017

Journal of Travel Research

457

266

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“…However, these and other recent studies that use sentiment analysis to create prediction features employ it to predict the polarity of sentiment and not the rating itself (Berezina et al, 2016;Bjørkelund et al, 2012;Calheiros et al, 2017;Duan et al, 2016;Han et al, 2016;He et al, 2017;Hu and Chen, 2016;Marcheggiani et al, 2014;Markopoulos et al, 2015;Zheng and Ye, 2009).…”

Section: Related Workmentioning

confidence: 99%

Hotel online reviews: creating a multi-source aggregated index

António¹,

Almeida

Nunes

et al. 2018

IJCHM

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Purpose This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems. Design/methodology/approach This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating. Findings Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment. Originality/value This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languages.

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“…According to Markopoulos et al (2015), the greater part of the research in sentiment analysis has been focused on online texts written in English and especially on movie and product reviews. Thus, the literature on other languages and domains is rather limited [40]. As a means of resolving this, they applied a machine-learning approach (which has been shown to be more accurate than semantic orientation approaches) for hotel reviews from the Greek version of TripAdvisor.…”

Section: Text Mining and Analysis In Literaturementioning

confidence: 99%

Perceptions of Agritourism and Cooperation: Comparisons between an Island and a Mountain Region in Greece

2019

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The lack of understanding and definitional inconsistencies regarding agritourism and the importance of cooperation in sustaining this kind of tourism are underlined in the literature. This study analyzes the perceptions of agritourism and cooperation from actors in the sector using a plurality of methods, including unsupervised (a) text mining and (b) sentiment analysis with the use of a lexical database, as well as (c) supervised qualitative data analysis. Based on the assumption that destinations with different geographic characteristics have different features and products, two different destinations as for its accessibility and tourism recognition were selected for comparison: (a) an island—Lesvos in the North Aegean Sea, and (b) a continental mountain region—Plastiras Lake, in Greece. The data were collected from personal in-depth interviews and with the use of semi-structured questionnaires. From a methodological perspective, all three methods provided unique insights on the study’s themes, and the overall image of agritourism and cooperation was positive. A common understanding seems important for cooperation and networking; however, training is needed not only for effective promotion of agritourism, but also for cooperation techniques, benefits, trust-building mechanisms and best practices.

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Sentiment Analysis of Hotel Reviews in Greek: A Comparison of Unigram Features

Cited by 21 publications

References 9 publications

Sentiment Analysis in Tourism: Capitalizing on Big Data

Sentiment Analysis in Tourism: Capitalizing on Big Data

Hotel online reviews: creating a multi-source aggregated index

Perceptions of Agritourism and Cooperation: Comparisons between an Island and a Mountain Region in Greece

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