Dealing with High Dimensional Sentiment Data Using Gradient Boosting Machines

Athanasiou, Vasileios; Μaragoudakis, Manolis

doi:10.1007/978-3-319-44944-9_42

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…In [40] and [41] we can see that ANNs have been used as well in order to mine and classify opinions from customers in different environments. Other ML methodologies that have also been applied to sentiment analysis are Latent Dirichlet Allocation [101,104,183] and [111], C4.5 and classification and regression trees [76] and [80], locally weighted linear regression [146], N-Gram classification [136], contrast targeted association rule mining [173], fuzzy logic algorithms [177] and [81], gradient boosting [116], multivariate regression [144] and natural language processing [106]. A research work performed by [45] also showcases how clustering can be applied to Sentiment Analysis by using ANFIS, combined with a dimensionality reduction approach to simplify the algorithm's offline training time.…”

Section: Sentiment Analysis and Satisfaction Degreementioning

confidence: 99%

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Guerra-Montenegro

Sánchez-Medina

Laña

et al. 2021

Applied Soft Computing

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This research work presents a detailed survey about Computational Intelligence (CI) applied to various Hotel and Travel Industry areas. Currently, the hospitality industry's interest in data science is growing exponentially because of their expected margin of profit growth. In order to provide precise state of the art content, this survey analyzes more than 160 research works from which a detailed categorization and taxonomy have been produced. We have studied the different approaches on the various forecasting methods and subareas where CI is currently being used. This research work also shows an actual distribution of these research efforts in order to enhance the understanding of the reader about this topic and to highlight unexploited research niches. A set of guidelines and recommendations for future research areas and promising applications are also presented in a final section.

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Section: Sentiment Analysis and Satisfaction Degreementioning

confidence: 99%

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Guerra-Montenegro

Sánchez-Medina

Laña

et al. 2021

Applied Soft Computing

View full text Add to dashboard Cite

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Attitudes towards machine translation and languages among travelers

Carvalho

Ramires

Iglesias

2023

Inf Technol Tourism

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Machine translation (MT), i.e., automatic translation, is a growing field in artificial intelligence with huge impacts on societies and businesses. Despite its importance for traveling and tourism communication, it has not been approached within tourism research. This study aims to fill this gap in knowledge by analyzing how attitudes toward machine translation are related to tourists’ profiles, travel behaviors, and language mindsets. It comprises two parts. The first one concerns a sample of 2535 individuals, while the second concerns a sub-sample of 907 language tourists (LTs). Specific research goals are set for each study: (1) to compare individuals with opposing viewpoints on the importance of MT in terms of profiles and attitudes toward languages; and (2) to understand how LTs’ profiles and travel experiences differed according to their agreement with the importance of MT in their most significant language trip. Statistical exploratory and inferential analyses have been conducted. We conclude that those with more positive views of MT tend to be younger and less educated, report poorer language skills, and attribute greater importance to the role of English as a lingua franca. Concerning LTs, those who rate MT as less important are more likely to have acquired language skills formally, engage more in cultural activities, and have closer contact with locals during their language trips. Acknowledging the role of MT in their most significant language trip is neither associated with a more unfavorable attitude towards the role of language in tourism nor with perceived diminished travel outcomes.

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Dealing with High Dimensional Sentiment Data Using Gradient Boosting Machines

Cited by 2 publications

References 11 publications

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Attitudes towards machine translation and languages among travelers

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