A Conceptual Paper: Text Mining Exploration for Early Identification of Poverty in Yogyakarta
Purpose: This study aims to explore information about poverty related communities through social media Twitter to get the initial identification of poverty in the Yogyakarta region.
Background: Poverty in Indonesia is still a problem of the government which needs more attention so that poverty rates decrease, especially in Yogyakarta. The current poverty estimation by the government still uses static variables and to get the data requires more effort.
Design/Methodology/Approach: The keyword used to retrieve data on Twitter is based on word cloud processing results from journals or papers about poverty. The data used in this research is Twitter data in a certain period that uses Indonesian. Text data is processed using a machine learning method approach to training and testing data. The algorithm used to do text mining in this research is Naïve Bayes Classifier by producing 3 classes of sentiment analysis which are positive, negative and neutral. The classification accuracy in this study was 66% with the data used in the study to 7000 data.
Results/Findings: This result can be an initial identification for decision makers in undertaking poverty alleviation efforts in an area and can be used as a new dynamic variable for estimating poverty in Indonesia together with static variables from the government.
Conclusion and Implications: The method used is expected to be able to identify poverty in very early warning time.
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