Sentiment Analysis of Sirekap Tweets Using CNN Algorithm

Abstract

Background: The research investigates the application of deep learning models for sentiment analysis on Twitter data related to Indonesia's Sirekap system. Sentiment analysis is crucial for understanding public opinion and enhancing the transparency and reliability of election result recapitulation processes. Objective: The objective of this study is to compare the performance of Convolutional Neural Networks (CNN) and CNN-LSTM models in analyzing sentiments from tweets about the Sirekap system. The study aims to identify the most effective model and preprocessing techniques to improve sentiment classification accuracy. Methods: A comprehensive data preprocessing pipeline was implemented, including cleansing, case folding, tokenizing, normalization, stopword removal, and stemming. To address class imbalance, the SMOTE technique was applied. The models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. Pre-trained word embeddings were used to enhance model performance. Results: The CNN model achieved an accuracy of 85.90%, outperforming the CNN-LSTM model, which achieved 79.91% accuracy. Additionally, the CNN model demonstrated superior precision, recall, and F1-score metrics compared to the CNN-LSTM model. The thorough preprocessing and handling of class imbalance significantly contributed to the enhanced performance of the CNN model. Conclusion: The research emphasizes the effectiveness of deep learning approaches, particularly CNNs, in sentiment analysis tasks. The findings highlight the importance of comprehensive preprocessing and class imbalance handling. The use of pre-trained word embeddings and various evaluation metrics ensures robust model performance. These insights contribute to improving the accuracy and efficiency of sentiment classification, thereby enhancing the reliability and transparency of election result recapitulation processes.