SENTIMENT ANALYSIS OF CYBERBULLYING USING BIDIRECTIONAL LONG SHORT TERM MEMORY ALGORITHM ON TWITTER - Safitri, Anisa Ika; Bayu Sasongko, Theopilus (Informatika, Universitas Jenderal Soedirman, 2024)
Cyberbullying on social media such as Twitter is becoming an increasing social problem in today's society. Cyberbullying has a negative influence on mental health, increasing the risk of anxiety, sadness, and even suicide. The purpose of this research is to develop a model to classify tweets that contain or do not contain cyberbullying by applying the BiLSTM technique to sentiment analysis on Twitter. In this research, Word2Vec is used to weight each word in a tweet. The initial stage in this research is data collection with a total dataset of 47,692 tweets generated by Kaggle, preprocessing which consists of data cleaning, removing duplicates, case folding, tokenizing, stopword removal and lemmatization, classification and evaluation. This research uses the Bidirectional Long Short-Term Memory (Bi-LSTM) method and identifies patterns associated with bullying on social media. Testing uses Confusion Matrix and the results on classification show accuracy of 82.29%, precision of 82,04%, recall of 81,95% and F1-Score 81,89%. This sentiment analysis technique is expected to be the first step to combat and avoid cyberbullying on the Twitter platform. From several tests of existing reference algorithms, the classification accuracy performed includes having good performance.