ShinyTopic: R Shiny Interactive Topic Modelling for Public Policy Narrative Case Study
-
Lukmanul Hakim
*
Universitas Madani, Yogyakarta,
Indonesia
https://orcid.org/0009-0009-8412-0651
- Fadhilah Dhinur Aini Universitas Madani, Yogyakarta, Indonesia (*) Corresponding Author
Abstract
The collection of unstructured text often yields a huge pile of raw data that cannot be easily measured with qualitative analysis. It is necessary to use a more varied form of assessment in order to allow researchers to explore large-scale collection of texts in an efficient manner. Using computer-aided to automate textual analysis offers them new opportunities rather than manual coding specifically for large amount of data, thus can saving resources. Topic model is one of the most common approach within the methodological domain of text mining and NLP. However, researcher in particular studies face technical barrier that limit systematic and replicable text analysis. ShinyTopic is web application built with R and Shiny packages that enable non-programmer who wish to perform topic models unifies with data import, pre-processing, topic analysis, metric assessment and annotation, visualization and result presentation. It gives research community to get collaborative benefit by simplifying complex methodologies for different subject. For demonstration, 200 documents from news outlet were processes to illustrate how user can perform complete analytical workflows-begin with data import and ended to result summary. In spite of its advantages, there are some limitations of ShinyTopic in term of language structure preprocessing and stemming of Indonesian text. To overcome that, a custom function was build inside core program of ShinyTopic yet less accurate for Indonesian language structures which still need to improve.
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