Text Classification Performance Optimization Based on Aspect-Based Analysis and Hybrid Deep Learing Model
DOI:
https://doi.org/10.33022/ijcs.v13i3.4034Keywords:
Aspect-based analysis, CNN-LSTM, Deep learning, Palestine-Israel, Text classificationAbstract
The conflict between Palestine and Israel has generated strong debates and reactions on social media, including in Indonesia. Public perception of various aspects is certainly important to identify issues in the Palestinian-Israeli conflict. However, the process of manually classifying aspects of the Palestinian-Israeli conflict requires human resources and considerable time. This research aims to explore the views of Indonesians on the Palestinian-Israeli conflict through sentiment analysis based on aspects of Territory, Religion, Politics, and History. Using deep learning technology, specifically a combination model of Convolutional Neural Networks with Long Short-Term Memory (CNN-LSTM), this research analyzes opinion and views data collected from X social media platform (Twitter). This research shows the results of the dataset obtained that the Political aspect dominates more than other aspects. The model evaluation results obtained an accuracy value of 96%, which indicates that the model's ability to classify X users' sentiments towards the Palestinian-Israeli conflict achieved a high level of success.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Salsabila Rabbani, Agustin, Susandri, Rahmiati, M. Khairul Anam

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.