Discovering evolutionary theme patterns from text: an exploration of temporal text mining

By Qiaozhu Mei, Chengxiang Zhai. 2005

650 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

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Citations in the corpus

Quantifying counts and costs via classification (2008, methodology): “…the less common work that acknowledges that the training and test distributions may differ, the estimate of the class distribution is used only in order to improve the individual classifications, e.g. via ROC analysis (Provost and Fawcett 2001; Fawcett 2003; Mei and Zhai 2005; Saerens et al. 2002).”
Event Detection in Social Streams (2012, methodology): “This method has been studied extensively by the text mining community in the context of the topic detection and tracking problem [4, 5, 7, 14, 17, 22, 23, 24].”
Detecting communities and their evolutions in dynamic social networks—a Bayesian approach (2011, background): “Mei and Zhai (2005) extracted latent themes from text and used the evolution graph of themes for temporal text mining.”
Applications of Topic Models (2017, background): “1Mei and Zhai [2005] present an earlier example of contemporary news analysis (i.”
Evidence for the network perspective on organizational learning (2008, methodology): “Main-path analysis associates the links (citations) to their connectivity qualities; relative entropy (Kullback & Leibler, 1951) will be used as a measure to evaluate whether the distributions of references changed between papers to such an extent that a pathdependency was created ( Mei & Zhai, 2005”
MONIC: modeling and monitoring cluster transitions (2006, background): “In [8], the emphasis is on adapting the clusters, while in [7] a topic evolution graph is built and used to trace topic transitions, i.”
Discovering Diverse and Salient Threads in Document Collections (2012, background): “A variety of papers from the topic tracking literature are broadly related to our work (Mei and Zhai, 2005; Blei and Laﬀerty, 2006; Leskovec et al., 2009; Ahmed and Xing, 2010).”
Topic sentiment mixture: modeling facets and opinions in weblogs (2007, methodology): “A lot of previous work has shown the effectiveness of mixture of multinomial distributions (mixture language models) in extracting topics (themes, subtopics) from either plain text collections or contextualized collections [9, 1, 16, 15, 17, 12].”
External validity of sentiment mining reports: Can current methods identify demographic biases, event biases, and manipulation of reviews? (2014, methodology): “See the list of papers we found by our query for event-bias detection [12,41,62–77].”
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks (2008, methodology): “[12] extracted latent themes from text and used the evolution graph of themes for temporal text mining.”
A probabilistic approach to spatiotemporal theme pattern mining on weblogs (2006, methodology): “When no domain knowledge is available as in our experiments, we follow [21] to determine the number of themes by enumerating multiple possible values of k and drop the themes with a significant low value of 1 |C| ∑ d∈C p(θ|d).”
Automatic labeling of multinomial topic models (2007, methodology): “Indeed, when labeling topic models manually, most people prefer phrases [17, 16, 18, 24].”
On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking (2008, background): “Considering time information for the task of identifying and tracking topics in time-stamped text data is the focus of recent studies (e.g. [4, 7, 10, 11]).”