Analisis Sentimen Pengguna Twitter Terhadap Serangan Moskow oleh ISIS dengan Algoritma Naive Bayes - Cindy Pratiwi; Dodi Vionanda; Fayyadh Ghaly (UNP Journal of Statistics and Data Science, 2025)
This study aims to analyze public sentiment towards the ISIS attack in Moscow, Russia on March 22, 2024 through twitter data using the Naive Bayes classification method. The attack had a significant impact on people's perceptions and reactions as reflected in the tweets of twitter social media users. To analyze this, 3005 English tweets from 22 March 2024 to 30 April 2024 relating to the event were collected using the crawling method with the phyton programming language. Preprocessing was done on the data to clean the data, then data labeling was done using phyton TextBlob. Naive Bayes algorithm is used to classify the sentiment of tweets into positive, and negative classes. The results of the research using Naive Bayes show that public sentiment tends to be negative towards the attacks that occurred. Naive Bayes classification results are quite good with an accuracy value of 70%, but there is an imbalance of data that tends to be biased towards negative sentiment. This research provides insight into how public opinion responds to events that occur and the performance of the Naive Bayes model in classification.