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Optimal strategies to perform multilingual analysis of social content for a novel dataset in the tourism domain.

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Title: Optimal strategies to perform multilingual analysis of social content for a novel dataset in the tourism domain.
Authors: Masson, Maxime1,2 (AUTHOR) maxime.masson@univ-pau.fr, Agerri, Rodrigo2 (AUTHOR) rodrigo.agerri@ehu.eus, Sallaberry, Christian1 (AUTHOR) christian.sallaberry@univ-pau.fr, Bessagnet, Marie-Noelle1 (AUTHOR) marie-noelle.bessagnet@univ-pau.fr, Le Parc Lacayrelle, Annig1 (AUTHOR) annig.lacayrelle@univ-pau.fr, Roose, Philippe1 (AUTHOR) philippe.roose@univ-pau.fr
Source: Knowledge-Based Systems. Sep2025, Vol. 326, pN.PAG-N.PAG. 1p.
Subjects: Tourism, Natural language processing, Acquisition of data, Social media, Data mining, Sentiment analysis
Abstract: The rising influence of social media platforms in various domains, including tourism, has highlighted the growing need for efficient and automated Natural Language Processing (NLP) strategies to take advantage of this valuable resource. However, the transformation of multilingual, unstructured, and informal texts into structured knowledge still poses significant challenges, most notably the never-ending requirement for manually annotated data to train deep learning classifiers. In this work, we study different NLP techniques to establish the best ones to obtain competitive performances while keeping the need for training annotated data to a minimum. To do so, we built the first publicly available multilingual dataset (French, English, and Spanish) for the tourism domain, composed of tourism-related tweets. The dataset includes multilayered, manually revised annotations for Named Entity Recognition (NER) for Locations and Fine-grained Thematic Concepts Extraction mapped to the Thesaurus of Tourism and Leisure Activities of the World Tourism Organization, as well as for Sentiment Analysis at the tweet level. Extensive experimentation comparing various few-shot and fine-tuning techniques with modern language models demonstrate that modern few-shot techniques allow us to obtain competitive results for all three tasks with very little annotation data: 5 tweets per label (15 in total) for Sentiment Analysis, 30 tweets for Named Entity Recognition of Locations and 1K tweets annotated with fine-grained thematic concepts, a highly fine-grained sequence labeling task based on an inventory of 315 classes. We believe that our results, grounded in a novel dataset, pave the way for applying NLP to new domain-specific applications, reducing the need for manual annotations and circumventing the complexities of rule-based, ad-hoc solutions. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge-Based Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Optimal strategies to perform multilingual analysis of social content for a novel dataset in the tourism domain.
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  Data: <searchLink fieldCode="AR" term="%22Masson%2C+Maxime%22">Masson, Maxime</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> maxime.masson@univ-pau.fr</i><br /><searchLink fieldCode="AR" term="%22Agerri%2C+Rodrigo%22">Agerri, Rodrigo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> rodrigo.agerri@ehu.eus</i><br /><searchLink fieldCode="AR" term="%22Sallaberry%2C+Christian%22">Sallaberry, Christian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> christian.sallaberry@univ-pau.fr</i><br /><searchLink fieldCode="AR" term="%22Bessagnet%2C+Marie-Noelle%22">Bessagnet, Marie-Noelle</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> marie-noelle.bessagnet@univ-pau.fr</i><br /><searchLink fieldCode="AR" term="%22Le+Parc+Lacayrelle%2C+Annig%22">Le Parc Lacayrelle, Annig</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> annig.lacayrelle@univ-pau.fr</i><br /><searchLink fieldCode="AR" term="%22Roose%2C+Philippe%22">Roose, Philippe</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> philippe.roose@univ-pau.fr</i>
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  Data: <searchLink fieldCode="JN" term="%22Knowledge-Based+Systems%22">Knowledge-Based Systems</searchLink>. Sep2025, Vol. 326, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Tourism%22">Tourism</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Social+media%22">Social media</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The rising influence of social media platforms in various domains, including tourism, has highlighted the growing need for efficient and automated Natural Language Processing (NLP) strategies to take advantage of this valuable resource. However, the transformation of multilingual, unstructured, and informal texts into structured knowledge still poses significant challenges, most notably the never-ending requirement for manually annotated data to train deep learning classifiers. In this work, we study different NLP techniques to establish the best ones to obtain competitive performances while keeping the need for training annotated data to a minimum. To do so, we built the first publicly available multilingual dataset (French, English, and Spanish) for the tourism domain, composed of tourism-related tweets. The dataset includes multilayered, manually revised annotations for Named Entity Recognition (NER) for Locations and Fine-grained Thematic Concepts Extraction mapped to the Thesaurus of Tourism and Leisure Activities of the World Tourism Organization, as well as for Sentiment Analysis at the tweet level. Extensive experimentation comparing various few-shot and fine-tuning techniques with modern language models demonstrate that modern few-shot techniques allow us to obtain competitive results for all three tasks with very little annotation data: 5 tweets per label (15 in total) for Sentiment Analysis, 30 tweets for Named Entity Recognition of Locations and 1K tweets annotated with fine-grained thematic concepts, a highly fine-grained sequence labeling task based on an inventory of 315 classes. We believe that our results, grounded in a novel dataset, pave the way for applying NLP to new domain-specific applications, reducing the need for manual annotations and circumventing the complexities of rule-based, ad-hoc solutions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Knowledge-Based Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=188445548
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        Value: 10.1016/j.knosys.2025.114001
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        Text: English
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      – SubjectFull: Tourism
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      – SubjectFull: Natural language processing
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      – SubjectFull: Acquisition of data
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      – SubjectFull: Social media
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      – SubjectFull: Sentiment analysis
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      – TitleFull: Optimal strategies to perform multilingual analysis of social content for a novel dataset in the tourism domain.
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              Text: Sep2025
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