The Decision Tree Algorithm on Sentiment Analysis: Russia and Ukraine War
Sentiment analysis is a method to automatically
understand, extract, and process a text data to obtain an
information or an opinion contained in said data. This is done
in order to determine whether an opinion is positive, negative
or neutral in relevance to a object, product or topic. This
research is aimed to implement the decision tree algorithm into
our sentiment analysis application regarding the Russian and
Ukraine war. This issue has been a trending topic on twitter
and is attracting many attention from the public. Therefore,
we gathered their opinions and analyze them using the decision
tree algorithm. We managed to gather 1.069 data and obtain
the results along with their average scores by conducting 15
tests using three different data partitions. Based on the results
obtained we concluded that the data partition of 80% training
data and 20% testing data gained the highest average score. The
result was 85.61% for accuracy, 86.27% for the precision, and
86.01% for the recall.
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