Exploring Geographical Crowd’s Emotions with Twitter
| hal.structure.identifier | Kyoto University | |
| dc.contributor.author | WAKAMIYA, Shoko | |
| hal.structure.identifier | Institut de Recherche de l'Ecole Navale [IRENAV] | |
| dc.contributor.author | BELOUAER, Lamia | |
| hal.structure.identifier | Institut de Recherche de l'Ecole Navale [IRENAV] | |
| dc.contributor.author | BROSSET, David | |
| hal.structure.identifier | Kyoto University | |
| dc.contributor.author | KAWAI, Yukiko | |
| hal.structure.identifier | Institut de Recherche de l'Ecole Navale [IRENAV] | |
| dc.contributor.author | CLARAMUNT, Christophe | |
| hal.structure.identifier | University of Hyogo | |
| dc.contributor.author | SUMIYA, Kazutoshi | |
| dc.date.accessioned | 2021-05-14T09:55:16Z | |
| dc.date.available | 2021-05-14T09:55:16Z | |
| dc.date.issued | 2015-03 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/77676 | |
| dc.description | The research introduced in this paper develops a semantic model whose objective is to analyze the geographical and emotion-based distribution of tweets at a large country scale. The approach extracts and categorizes tweets based on semantic orientations ofterms in a dictionary, and explores their spatial and temporal distribution. Tweets are classified into different emotional classes, qualified and valued using differentinterval distributions that favor identification of significant trends that are compared to some of the main properties of the underlying geographical space. The whole approach is applied to a large tweets database in Japan, and illustrated by some experimental but real data that trigger some surprising and puzzling outcomes that are discussed in the paper. | |
| dc.description.abstractEn | The research introduced in this paper develops a semantic model whose objective is to analyze the geographical and emotion-based distribution of tweets at a large country scale. The approach extracts and categorizes tweets based on semantic orientations ofterms in a dictionary, and explores their spatial and temporal distribution. Tweets are classified into different emotional classes, qualified and valued using differentinterval distributions that favor identification of significant trends that are compared to some of the main properties of the underlying geographical space. The whole approach is applied to a large tweets database in Japan, and illustrated by some experimental but real data that trigger some surprising and puzzling outcomes that are discussed in the paper. | |
| dc.language.iso | en | |
| dc.subject | réseaux sociaux | |
| dc.subject | sig | |
| dc.title.en | Exploring Geographical Crowd’s Emotions with Twitter | |
| dc.type | Article de revue | |
| dc.subject.hal | Informatique [cs]/Théorie de l'information [cs.IT] | |
| bordeaux.journal | DBSJ journal | |
| bordeaux.page | 77-82 | |
| bordeaux.volume | 13 | |
| bordeaux.hal.laboratories | Institut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295 | * |
| bordeaux.issue | 1 | |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.institution | INRAE | |
| bordeaux.institution | Arts et Métiers | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-01208062 | |
| hal.version | 1 | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-01208062v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=DBSJ%20journal&rft.date=2015-03&rft.volume=13&rft.issue=1&rft.spage=77-82&rft.epage=77-82&rft.au=WAKAMIYA,%20Shoko&BELOUAER,%20Lamia&BROSSET,%20David&KAWAI,%20Yukiko&CLARAMUNT,%20Christophe&rft.genre=article |
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