Sunday, 20 de April de 2014

Ficha del recurso:

Fuente:

Vínculo original en SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL : 873-874 2010
Liu, Y; Yu, XH; Huang, XJ; An, AJ

Última actualización:

Thursday, 24 de March de 2011

Entrada en el observatorio:

Thursday, 24 de March de 2011

Idioma:

Inglés

Archivado en:


S-PLSA(+): Adaptive Sentiment Analysis with Application to Sales Performance Prediction

Analyzing the large volume of online reviews would produce useful knowledge that could be of economic values to vendors and other interested parties. In particular, the sentiments expressed in the online reviews have been shown to be strongly correlated with the sales performance of products. In this paper, we present an adaptive sentiment analysis model called S-PLSA(+), which aims to capture the hidden sentiment factors in the reviews with the capability to be incrementally updated as more data become available. We show how S-PLSA(+)can be applied to sales performance prediction using an ARSA model developed in previous literature. A case study is conducted in the movie domain, and results from preliminary experiments confirm the effectiveness of the proposed model.