%0 Conference Paper
%F Oral 
%T A Joint Model for Topic-Sentiment Evolution over Time
%+ Equipe de Recherche en Ingénierie des Connaissances (ERIC)
%+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC)
%A Dermouche, Mohamed
%A Velcin, Julien
%A Khouas, Leila
%A Loudcher, Sabine
%< avec comité de lecture
%B 2014 IEEE International Conference on Data Mining (ICDM)
%C Shenzhen, China
%I IEEE
%S Data Mining (ICDM), 2014 IEEE International Conference on
%8 2014-12-14
%D 2014
%R 10.1109/ICDM.2014.82
%K joint topic sentiment models
%K time series
%K trend analysis
%K topic models
%K sentiment analysis
%K opinion mining
%Z Computer Science [cs]/Web
%Z Computer Science [cs]/Document and Text Processing
%Z Computer Science [cs]/Information Retrieval [cs.IR]
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]
%Z Statistics [stat]/Machine Learning [stat.ML]Conference papers
%X —Most existing topic models focus either on extracting static topic-sentiment conjunctions or topic-wise evolution over time leaving out topic-sentiment dynamics and missing the opportunity to provide a more in-depth analysis of textual data. In this paper, we propose an LDA-based topic model for analyzing topic-sentiment evolution over time by modeling time jointly with topics and sentiments. We derive inference algorithm based on Gibbs Sampling process. Finally, we present results on reviews and news datasets showing interpretable trends and strong correlation with ground truth in particular for topic-sentiment evolution over time.
%G English
%2 https://hal.science/hal-01762995/document
%2 https://hal.science/hal-01762995/file/icdm_short.pdf
%L hal-01762995
%U https://hal.science/hal-01762995
%~ UNIV-LYON1
%~ UNIV-LYON2
%~ ERIC
%~ LABEXIMU
%~ UDL
%~ UNIV-LYON