Social-Child-Case Document Clustering based on Topic Modeling using Latent Dirichlet Allocation

Abstract

Children are the future of the nation. All treatment and learning they get would affect their future. Nowadays, there are various kinds of social problems related to children. To ensure the right solution to their problem, social workers usually refer to the social-child-case (SCC) documents to find similar cases in the past and adapting the solution of the cases. Nevertheless, to read a bunch of documents to find similar cases is a tedious task and needs much time. Hence, this work aims to categorize those documents into several groups according to the case type. We use topic modeling with Latent Dirichlet Allocation (LDA) approach to extract topics from the documents and classify them based on their similarities. The Coherence Score and Perplexity graph are used in determining the best model. The result obtains a model with 5 topics that match the targeted case types. The result supports the process of reusing knowledge about SCC handling that ease the finding of documents with similar cases

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“…The number of topics was selected based on the perplexity score [ 73 ] and coherence score [ 70 ] as the quantitative information indicators to exclude the subjective judgment of the researcher. In general, topic modeling becomes more stable, and each topic consistently constitutes a clear subject when the perplexity score decreases [ 74 ], whereas the coherence score increases [ [75] , [76] ]. The number of topic options was narrowed down to 16, 17, 18, and 19 based on their low perplexity and high coherence scores ( Fig.…”

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“…Topic modelling secara statistic bekerja dengan cara mengeksplorasi dokumen yang diberikan dan merepresentasikan mereka sebagai kumpulan istilah yang sering muncul bersamaan dalam dokumen [1]. Menurut Gurcan, LDA pada topic modelling adalah suatu metode pendekatan yang generatif yang digunakan untuk menemukan pola semantic yang ada pada suatu korpus dokumen yang relatif tidak terstruktur [2].…”