Optimizing Content Marketing Using Automatic Keyword Extraction To Get Topic Prediction

Savitri Indriyani

doi:10.36418/syntax-literate.v7i12.10782

Savitri Indriyani Swiss German University, Indonesia

DOI: https://doi.org/10.36418/syntax-literate.v7i12.10782

Abstract

Digital news with a variety topics is abundant on the internet. The problem is to classify news based on its appropriate category to facilitate user to find relevant news rapidly. The manual categorization of text documents requires a lot of financial and human resources to do the process. In order to get so, topic modeling usually used to classify documents. In the used topic models (LSA, LDA) each word in the corpus of vocabulary is connected with one or more topics with a probability, as estimated by the model. Many (LDA, LSA) models were built with different values of coherence and pick the one that produces the highest coherence value. Based on the result, we summarized some points, three models above can answer the question in Research Question, those models can be applied in the future to company’s automation prosess of determining topic automatically. LDA using BOW and LSA using BOW would be priority option to be applied.

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