  The topic modeling discovers the latent topic probability of the given text
documents. To generate the more meaningful topic that better represents the
given document, we proposed a new feature extraction technique which can be
used in the data preprocessing stage. The method consists of three steps.
First, it generates the word/word-pair from every single document. Second, it
applies a two-way TF-IDF algorithm to word/word-pair for semantic filtering.
Third, it uses the K-means algorithm to merge the word pairs that have the
similar semantic meaning.
  Experiments are carried out on the Open Movie Database (OMDb), Reuters
Dataset and 20NewsGroup Dataset. The mean Average Precision score is used as
the evaluation metric. Comparing our results with other state-of-the-art topic
models, such as Latent Dirichlet allocation and traditional Restricted
Boltzmann Machines. Our proposed data preprocessing can improve the generated
topic accuracy by up to 12.99\%.
