Multi-Level and Multiple Aspect Semantic Trajectory Model: Application to the Tourism Domain

Cayèré, Cécile; Sallaberry, Christian; Faucher, Cyril; Bessagnet, Marie-Noelle; Roose, Philippe; Masson, Maxime; Richard, Jérémy

doi:10.3390/ijgi10090592

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Pour y répondre, nous utilisons des modules appartenant à six catégories différentes. Les données transitent entre les modules dans un fichier qui instancie notre modèle de trajectoire sémantique (Cayèré et al, 2021). Dans un premier temps, les traces brutes sont extraites, nettoyées et les trajectoires sont construites durant la phase de pré-traitement.…”

Section: Figure 3 Chaîne De Traitement De Trajectoires De Mobilité Co...unclassified

“…Une chaîne de traitement est construite à partir de modules situés sur un serveur distant qui sont requêtés, un par un, lors de l'exécution de la chaîne. Les données qui transitent entre les modules sont une instanciation de notre modèle de trajectoire sémantique multiple aspects (Cayèré et al, 2021). Dans de futurs travaux, nous proposerons de nouveaux modules concernant, entre autres, le calcul de similarité entre deux trajectoires sémantiques.…”

Section: Conclusion Et Perspectivesunclassified

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INFORSID 2020 Forum Jeunes Chercheuses Jeunes Chercheurs

Jamra,

Andriamampianina,

Cayèré

et al. 2024

ROISI

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Cet article reprend une sélection de quatre des sept articles courts du Forum Jeunes Chercheuses Jeunes Chercheurs (JCJC) d'INFORSID 2020. Instantané du paysage de la recherche française par des doctorants en systèmes d'information, cet article considère les thèmes de la détection de signaux faibles dans les réseaux sociaux, des graphes temporels, des traces spatio-temporelles numériques dans le domaine du tourisme et du peuplement de bases de connaissances. ABSTRACT. This article presents a selection of four of the seven short articles from the Forum of junior researchers (JCJC) of INFORSID 2020. Snapshot of the French research landscape by doctoral students in information systems, this article considers the topics of the detection of weak signals in social networks, temporal graphs, digital traces in the field of tourism and populating knowledge bases. MOTS-CLÉS. Signaux faibles, Graphes temporels, Données spatio-temporelles, Bases de connaissances.

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Section: Figure 3 Chaîne De Traitement De Trajectoires De Mobilité Co...unclassified

Section: Conclusion Et Perspectivesunclassified

INFORSID 2020 Forum Jeunes Chercheuses Jeunes Chercheurs

Jamra,

Andriamampianina,

Cayèré

et al. 2024

ROISI

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“…Nine key features (phrase frequency, sentence score, sentiment shifter rules, sentiment knowledge, medical concept, word and sentence embedding, linguistic, and statistical knowledge) were fed to the model for the training of the model for the classification task. The set of features with their details of calculation and working are discussed in detail in [63]. Table 2 shows a description of these nine features, presented, for clarity, to the readers.…”

Section: Input Featuresmentioning

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Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

2022

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Sentiment Analysis (SA) is a technique to study people’s attitudes related to textual data generated from sources like Twitter. This study suggested a powerful and effective technique that can tackle the large contents and can specifically examine the attitudes, sentiments, and fake news of “E-learning”, which is considered a big challenge, as online textual data related to the education sector is considered of great importance. On the other hand, fake news and misinformation related to COVID-19 have confused parents, students, and teachers. An efficient detection approach should be used to gather more precise information in order to identify COVID-19 disinformation. Tweet records (people’s opinions) have gained significant attention worldwide for understanding the behaviors of people’s attitudes. SA of the COVID-19 education sector still does not provide a clear picture of the information available in these tweets, especially if this misinformation and fake news affect the field of E-learning. This study has proposed denoising AutoEncoder to eliminate noise in information, the attentional mechanism for a fusion of features as parts where a fusion of multi-level features and ELM-AE with LSTM is applied for the task of SA classification. Experiments show that our suggested approach obtains a higher F1-score value of 0.945, compared with different state-of-the-art approaches, with various sizes of testing and training datasets. Based on our knowledge, the proposed model can learn from unified features set to obtain good performance, better results than one that can be learned from the subset of features.

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Construction of Models for Evaluation and Development of Urban Tourism Resources Based on Artificial Intelligence

Liu

2022

Lecture Notes on Data Engineering and Communications Technologies

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Multi-Level and Multiple Aspect Semantic Trajectory Model: Application to the Tourism Domain

Cited by 5 publications

References 12 publications

INFORSID 2020 Forum Jeunes Chercheuses Jeunes Chercheurs

INFORSID 2020 Forum Jeunes Chercheuses Jeunes Chercheurs

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

Construction of Models for Evaluation and Development of Urban Tourism Resources Based on Artificial Intelligence

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