CLASSIFICATION OF PUBLIC OPINION REGARDING THE POLICIES OF THE CENTRAL GOVERNMENT VIA TWITTER DATA USING LATENT DIRICHLET ALLOCATION
This study presents one popular topological modeling technique used in many research studies, namely Latent Dirichlet Allocation (LDA). In the context of the text analysis, LDA provides a strong framework for organizing documents according to the relevant topic sentences in the text. This method effectively identifies the relationship between the words in the document and transfers them to the relevant topik-topik that are extracted from the aforementioned content. The purpose of the LDA in this study is to reveal the topological structure that is hidden in the collection of text documents. By grouping documents according to related keywords or themes, LDA facilitates more in-depth understanding of the range of topics covered in the aforementioned text. The results of this LDA modeling can be used for more in-depth analysis, such as topological segmentation, document classification, or more canggih recommendation system development. In summary, the use of LDA in this study allows researchers to obtain more information on the structure and content of a collection of complex text documents. Through the identification of the pola-pola that are missing from the text data, this study can make a significant contribution to the understanding and retrieval of the information contained in the text.
LDA, X, Government Policy.