Sentiment Analysis and Twitter Social Media Visualization Regarding the Omnibus Law Draft
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Abstract
This study will classify Twitter users' positive and negative opinions about the omnibus method using a frequency-inverse document frequency algorithm and a multi-layer perceptron method. The sentiment analysis process involves several stages, including B. Collecting and preprocessing data, calculating term weights using inverse term frequencies and document frequencies, and classifying data using multi-layer perceptrons. Additionally, the study visually represents Twitter's sentiment analysis results on the omnibus method. These visualizations include word cloud, top accounts, tweet frequency, hashtags, and sentiment. Three scenarios were considered to perform the classification experiments. Scenario 1 used 700 training data, scenario 2 used 800, and Scenario 3 used 900 training data. The findings show that the Term Frequency Inverse Document Frequency algorithm and the multi-layer perceptron method are adequate for sentiment analysis, with Scenario 3 yielding the highest accuracy rate of 88%.
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