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This paper presents a keyword extraction technique that can be used for tracking topics over time. In our work, keywords are a set of significant words in an article that gives high-level description of its contents to readers. Identifying keywords from a large amount of on-line news data is very useful in that it can
measure sentiment using a binary choice keyword algorithm and a multi-knowledge based approach is proposed using, Self-Organizing Maps and tourism domain knowledge in order to model sentiment. We develop a visual model to express this taxonomy of sentiment vocabulary and then apply this model to maximums and minimums in the
of the documents considered for context assessment contains the authors list, keywords list and list of document versioning time schedules. The experiments were conducted to assess the significance of the proposed model.
three different unsupervised learning methods for identifying topics among domain name keywords: spherical k-means clustering (SKM), Latent Dirichlet Allocation (LDA), and the Biterm Topic Model (BTM). We successfully infer semantically coherent groups of words in two independent data sets, finding that BTM topics are
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