Sentiment Analysis of Hotel Reviews in Greek: A Comparison of Unigram Features
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SVM and Naïve Bayes are the key machine learning methods used for sentiment analysis in the literature.
“Support Vector Machine (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.”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Markopoulos et al., 2015
For topic detection, SVM, Naïve Bayes, k-nearest neighbour, terminology and dictionary-based approaches, frequent words, latent-semantic indexing, sequential pattern mining, and cluster analysis were applied in the tourism context.
“In addition, for topic detection, SVM, Naïve Bayes, and k-nearest neighbour methods (as supervised machine learning approach) terminology and dictionary-based approach as well as frequent words, latent-semantic indexing, sequential pattern mining, and cluster analysis (as unsupervised techniques) were applied in the tourism context (Brob, 2013;Markopoulos et al., 2015;Höpken et al. 2016;Schmunk et”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Markopoulos et al., 2015
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References 16 · Cited by 35
- Authors
- Markopoulos, G., Mikros, G., Iliadi, A., & Liontos, M.
- Affiliations
- National and Kapodistrian University of Athens (GR)
- Corresponding author
- Markopoulos, George
- Venue
- Springer Proceedings in Business and Economics
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 373-383
- ISSN
- 2198-7246 (ISSN-L)
- 2198-7254
- OpenAlex
- W2261339705
- Cited by
- 35
- References
- 16
- Citations by year
- 2025: 6 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 4 · 2020: 5 · 2019: 3 · 2018: 3 · 2017: 3 · 2016: 1
- FWCI
- 4.65
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer proceedings in business and economics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- sentiment analysis, hotel reviews, Modern Greek, unigram features, support vector machine, online reviews
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