|
| << vorige | volgende >> |
Tijdschrift beschrijving
Alle jaargangen van het bijbehorende tijdschrift
Alle afleveringen van het bijbehorende jaargang
Alle artikelen van de bijbehorende aflevering
Details van artikel 3 van 65 gevonden artikelen
| Titel: |
Activity-based topic discovery |
| Auteur: |
Zhu, Dandan Fukazawa, Yusuke Karapetsas, Eleftheerios Ota, Jun |
| Verschenen in: |
Web intelligence and agent systems |
| Paginering: | Jaargang 12 (2014) nr. 2 pagina's 193-209 |
| Jaar: |
2014-03-26 |
| Inhoud: |
A topic model capable of assigning word pairs to associated topics is developed to explore people's activities. Considering that the form of word pairs led by verbs is a more effective way to express people's activities than separate words, we incorporate the word-connection model into the smoothed Latent Dirichlet Allocation (LDA) to ensure that the words are well paired and assigned to the associated topics. To quantitatively and qualitatively evaluate the proposed model, two datasets were built using Twitter posts as data sources: the wish-related and the geographical information-related datasets. The experiment results using the wish-related dataset indicate that the relatedness of words plays a key role in forming reasonable pairs, and the proposed model, word-pair generative Latent Dirichlet Allocation (wpLDA), performs well in clustering. Results obtained using the geographical information-related dataset demonstrate that the proposed model works well for discovering people's activities, in which the activities are understandably represented with an intuitive character. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 3 van 65 gevonden artikelen
| << vorige | volgende >> |