Sentiment Analysis of Post-Covid-19 Inflation Based on Twitter Using The K-Nearest Neighbor and Support Vector Machine Classification Methods
Analisis Sentimen Terhadap Inflasi Pasca Covid-19 Berdasarkan Twitter Dengan Metode Klasifikasi K-Nearest Neighbor dan Support Vector Machine
DOI:
https://doi.org/10.21070/ups.301Keywords:
COVID-19, Inflation, K-Nearest Neighbor, Sentiment Analysis, Support Vector MachineAbstract
The COVID-19 pandemic caused a crisis in global economic growth. The impact of injuries due to the COVID-19
pandemic has also caused price increases and an increase in the inflation rate. Inflation is a price increase caused
by a certain factor so that it has an impact on the prices of nearby goods which increase the circulation of money
in society to increase. Many people expressed their various opinions or criticisms of the post-COVID-19 price
increase policy on social media, one of which was via Twitter. Sentiment analysis was carried out to see how
public sentiment is towards the price increase policy after the COVID-19 pandemic, and these sentiments are
combined into multiclasses, namely positive, negative and neutral sentiments. This study aims to see and
compare the accuracy of the two classification methods, namely K-Nearest Neighbor (K-NN) and Support Vector
Machine (SVM) in the sentiment classification process.
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