We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
EXTRACCIÓN DE TEMAS EMERGENTES EN MICROBLOGS UTILIZANDO MODELOS DE TEMAS Y DISCRIMINACIÓN DE BITÉRMINOS.
- Authors
Quesada Grosso, Minor; Casasola Murillo, Édgar; Leoni de León, Antonio
- Abstract
Mining and exploitation of data in social networks has not only been the focus of many efforts, but despite of the resources and energy invested, it still remains a lot of work given its complexity. Specifically, the content of the texts published regularly at sites of microblogs (as Twitter.com) can be used to analyze trends. The latter are marked by emerging topics that are distinguished from others by a sudden and accelerated increment of popularity. In this way, the problem is to extract the topics and identifying which of those topics are trending. A recent solution, known as Bursty Biterm Topic Model (BBTM) is an algorithm for identifying trending topics, with a good level of performance in Twitter. However, it takes into account all the words, including those that are not representative of the trending topics. For this reason, this investigation offers an initial exploration for the application of discrimination of biterms used by BBTM to modeling trending topics.
- Publication
Káñina. Revista de Artes y Letras de la Universidad de Costa Rica, 2016, Vol 40, Issue 4, p33
- ISSN
0378-0473
- Publication type
Article