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A widely used topic modeling technique is Latent Dirichlet allocation.
We used topic modeling to synthesize the "topics" from 1540 comments.
In another approach, Majumder and Saha used topic modeling and parse structure similarity to identify informative sentences.
The frequently used topic of "Women doomed to live lives of suffering in the cruel prison of society and their time period" induced affects of sorrow and grief.
The team used topic modeling to analyze the language used in these descriptions to determine the proximity of each startup to other companies.
Three levels of coding were used: topic, category, and theme [ 18- 20].
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5 Topic community discovery Using topic correlations to find research topic communities.
We use topic distributions to measure clustering among formally recognized crime types.
Approximately 10% of time costs are saving by using topic features.
Instead of using Fisher similarity function for categorization using topic distribution probabilities, AdaBoost is employed to build a robust classifier using PLSA topic distribution probabilities as feature.
Thus, results from the top four methods prove the robustness of using topic features for phishing classification.
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