Analysis of Public Sentiment Text Clustering on Tax Increases using Orange Data Mining on Twitter
DOI:
https://doi.org/10.47709/brilliance.v5i1.5787Keywords:
Clustering, Data Analysis, Orange Data Mining, Tax, Text MiningAbstract
Taxes play an important role in the life of a nation and state, particularly in the implementation of national development. Recently, Indonesia issued a new policy to increase VAT to 12%. This policy has sparked a range of both negative and positive opinions from the public. As a result, various reactions and sentiments have been expressed by citizens regarding the policy. To analyze these public sentiments, text mining was carried out using the Orange Data Mining application, utilizing data from the Twitter platform to observe and evaluate Indonesian citizens' reactions. A total of 100 tweets were collected using relevant keywords to find content related to the policy. The results were then categorized into several sentiment groups based on the similarity of their content. After the text classification, the data was stored in a table showing the number of positive, negative, and neutral sentiments. This data was later visualized in a graph, which revealed that the most common reaction was disappointment, followed by confusion, enthusiasm, and lastly, anger. The results of this study indicate that many Indonesian citizens are disappointed with the VAT increase policy. Many believe that the government's use of tax funds has not been satisfactory. Therefore, the government is urged to improve its programs so that citizens can feel the benefits of the taxes they pay.
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