Identify User Behavior based on Tweet Type on twitter Platform using Mean Shift Clustering
DOI:
https://doi.org/10.30865/mib.v6i3.4329Keywords:
Mean Shift, Politic, Centrality, TF-IDF Vectorizer, User BehaviorAbstract
Twitter is a social media where users often get information from various fields. There are many problems with Twitter. For example, in Indonesia's political field, discussing the performance of the President of Indonesia and his staff who are not good, students and the public hold demonstrations in DKI Jakarta. They want the President of Indonesia to step down from office. When the problem is trending, some users have positive (praise) and negative (blasphemous) behavior, which is interesting to discuss in this study. Before the method stage, data preprocessing is carried out so that the data to be used becomes more efficient. Word weighting is also done using the TF-IDF Vectorizer. Then, the clustering method with the Mean Shift algorithm is applied to identify user behavior based on the type of tweet. This method can find information from a vast data set in a short time. Based on this algorithm, the results obtained are 67 clusters from the Mean Shift algorithm. From a total of 67 clusters obtained, 5 clusters were taken to identify user behavior. User behavior in clusters 0, 2, 3, and 4 is negative because it discusses the people who want the President of the Republic of Indonesia to resign from his position immediately. Meanwhile, user behavior in cluster 1 is positive because the topics discussed only information that the people of Lampung are already in Jakarta.
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