Topic Modeling based on Keywords and Context
Table 2: Datasets
['Dataset', 'Docs', 'Unique', 'Avg.', 'Cla-']
For the distinct review datasets (from Amazon) we predicted either the product, i.e., book, based on a review or the product category. The Wiki benchmarks are based on Wikipedia categories. We performed standard preprocessing, e.g., stemming, stopwords removal and removal of words that occurred only once in the entire corpus. All experiments ran on a server with a 64bit Ubuntu system, 100 GB RAM and 24 AMD cores operating at 2.6 GHz. Experiment 1: the number of (significantly) distinct topics |DT| An apparent disadvantage of HDP is that the variance in reduction seems more extreme than for TKM. HDP with κ=0.6 yields only about 30 topics for some datasets and 500 for others even though the datasets vary much less in terms of distinct words, documents and human-defined number of topics. suggests that discovering more than a few hundred topics seems less suitable. HDP’s convergence seems to depend more on the number of documents; that of TKM’s more on the number of unique words. The number of topics |DT| weakly depends on α – more so for TKM. Limiting the number of discovered topics, i.e., returning very similar topics, is an intrinsic property of TKM. A word influences the topic of nearby words directly and those of more distant words indirectly. Words with high keyword scores for a topic tend to pull other words to the same topic.