A Joint Model for Topic-Sentiment Evolution over Time

Mohamed Dermouche (1) , Julien Velcin (2) , Leila Khouas , Sabine Loudcher (1)

1 ERIC - Equipe de Recherche en Ingénierie des Connaissances
2 ERIC - Entrepôts, Représentation et Ingénierie des Connaissances

—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.

Mots clés: joint topic sentiment models; time series; trend analysis; topic models; sentiment analysis; opinion mining

Domaines: Web; Traitement du texte et du document; Recherche d'information [cs.IR]; Intelligence artificielle [cs.AI]; Machine Learning [stat.ML]

Mohamed Dermouche, Julien Velcin, Leila Khouas, Sabine Loudcher. A Joint Model for Topic-Sentiment Evolution over Time. 2014 IEEE International Conference on Data Mining (ICDM), Dec 2014, Shenzhen, China. ⟨10.1109/ICDM.2014.82⟩. ⟨hal-01762995⟩