Forough Poursabzi-Sangdeh

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= {Probabilistic topic models are important tools for indexing, summarizing, and analyzing large document collections by their themes. However, promoting end-user understanding of topics remains an open research problem. We compare labels generated by users given four topic visualization techniquesword lists, word lists with bars, word clouds, and network(More)
Content analysis, a labor-intensive but widely-applied research method, is increasingly being supplemented by computational techniques such as statistical topic modeling. However, while the discourse on content analysis centers heavily on reproducibility, computer scientists often focus more on increasing the scale of analysis and less on establishing the(More)
Effective text classification requires experts to annotate data with labels; these training data are time-consuming and expensive to obtain. If you know what labels you want, active learning can reduce the number of labeled documents needed. However, establishing the label set remains difficult. Annotators often lack the global knowledge needed to induce a(More)
Document classification and topic models are useful tools for managing and understanding large corpora. Topic models are used to uncover underlying semantic and structure of document collections. Categorizing large collection of documents requires hand-labeled training data, which is time consuming and needs human expertise. We believe engaging user in the(More)
= {Probabilistic topic models are important tools for indexing, summarizing, and analyzing large document collections by their themes. However, promoting end-user understanding of topics remains an open research problem. We compare labels generated by users given four topic visualization techniquesword lists, word lists with bars, word clouds, and network(More)
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