A Joint Model for Topic-Sentiment Evolution over Time

被引：41

作者：

Dermouche, Mohamed ^{[1
,2
]}

Velcin, Julien ^{[1
]}

Khouas, Leila ^{[2
]}

Loudcher, Sabine ^{[1
]}

机构：

[1] Univ Lyon ERIC LYON 2, Lyon, France

[2] AMI Software R&D, Montpellier, France

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) | 2014年

关键词：

joint topic sentiment models; time series; trend analysis; topic models; sentiment analysis; opinion mining;

D O I：

10.1109/ICDM.2014.82

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：773 / 778

页数：6

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