Bibliography – Topic Model (Longitudinal over Time)

Sorted by Tags:
Humanities (all)
Global Humanities | History of Humanities | Humanities Advocacy | Humanities Statistics | Humanities Surveys | Public Humanities | Social Groups and the Humanities | Value of the Humanities | Humanities and Economic Value
Humanities Organizations: Humanities Councils (U.S.) | Government Agencies | Foundations | Scholarly Associations
Humanities in: Asia (Eastern) | Asia (Southern) | Australasia | Europe | Latin America | United Kingdom | United States
Journalism & Media (all)
Lists of News Sources | Databases with News Archives | Journalism Statistics | Journalism Organizations | Non-profit or NGO Journalism Organizations | Media Bias | Press Freedom | Social Media | World & International
Corpus Collection (all)
Corpus Representativeness | Comparison Paradigms for WE1S Corpus: Archives | Canons | Corpus Linguistics | Editions
Data Science & Machine Learning (see also Topic Modeling) (all)
Artificial Intelligence | Big Data | Data Mining | Data Visualization (see also Topic Model Visualizations) | Hierarchical Clustering | Interpretation & Interpretability (see also Topic Model Interpretation) | Mapping | Natural Language Processing | Network Analysis | Sentiment Analysis | Statistical Methods | Text Analysis (see also Topic Modeling)Wikification | Word Embedding & Vector Semantics
Digital Humanities (all) (selected resources related to WE1S methods or issues; some items also included under "Data Science and Machine Learning" and "Topic Modeling")
Cultural & Social Approaches in DH | Distant Reading | Topic Modeling in DH

Wang, Chong, David Blei, and David Heckerman. “Continuous Time Dynamic Topic Models.” ArXiv:1206.3298 [Cs, Stat], 2012. http://arxiv.org/abs/1206.3298. Cite
Saria, Suchi, Daphne Koller, and Anna Penn. “Discovering Shared and Individual Latent Structure in Multiple Time Series.” ArXiv:1008.2028 [Cs, Stat], 2010. http://arxiv.org/abs/1008.2028. Cite
Hall, David, Daniel Jurafsky, and Christopher D. Manning. “Studying the History of Ideas Using Topic Models.” In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 363–371. Association for Computational Linguistics, 2008. https://web.stanford.edu/ jurafsky/hallemnlp08.pdf. Cite
McCallum, Andrew, and Xuerui Wang. Topics over Time: A Non-Markov Continuous-Time Model of Topical Trends, 2006. /paper/Topics-over-time%3A-a-non-Markov-continuous-time-of-Wang-McCallum/7f8abf25ca24b48450b4e535f41e2b8a87df73f5. Cite
Blei, David M., and John D. Lafferty. “Dynamic Topic Models.” In Proceedings of the 23rd International Conference on Machine Learning - ICML ’06, 113–120. Pittsburgh, Pennsylvania: ACM Press, 2006. https://doi.org/10.1145/1143844.1143859. Cite