Analyzing Sentiments in YouTube Comments on the Israel-Hamas War Using Deep Learning Techniques

The ongoing conflict between Israel and Hamas has sparked extensive debate and discussion on social media platforms, particularly YouTube. With the escalation of the conflict in 2023, sentiment analysis of YouTube comments has become crucial in understanding the general public’s perceptions and feelings about the Israel-Hamas War.

Social media platforms have provided a wealth of data for users to mine, offering valuable insights into public attitudes, particularly on war-related issues. Machine-learning algorithms, particularly deep learning techniques, have played a vital role in sentiment analysis, enhancing the understanding of user-generated data and complex sentiments.

Deep learning techniques, inspired by the brain’s structural and autonomous learning ability, have streamlined computational model development and outperformed standard machine learning methods in sentiment analysis. Recurrent neural networks (RNNs) have excelled in capturing subtle sentiments in the unstructured nature of YouTube comments, making them highly effective in analyzing user-generated content.

With the ability to handle sequential data, RNNs, including LSTM and GRU units, have proven essential for predictive tasks such as natural language understanding and sentiment classification. LSTM networks, in particular, enable RNNs to retain inputs over long periods by utilizing memory cells, enhancing their ability to discern the importance of data.

Recent studies have shown the effectiveness of deep learning algorithms, including CNNs and Bi-LSTM networks, in sentiment analysis of social media data. These models have achieved high classification accuracy and demonstrated superior performance compared to conventional neural networks. The bidirectional LSTM networks, which combine information from past and future time frames, have shown promise in minimizing delays and improving sentiment analysis results on social media platforms.

By implementing deep learning algorithms, researchers have successfully analyzed YouTube comments about the Israel-Hamas War, providing valuable insights into public opinion and sentiment. These insights can aid in conflict resolution efforts by identifying common themes, sentiments, and viewpoints in the ongoing conflict.

In conclusion, deep learning algorithms have proven to be effective tools in analyzing user-generated data and extracting valuable insights from social media platforms like YouTube. By harnessing the power of RNNs and LSTM networks, researchers can gain a deeper understanding of public opinion and sentiment on sensitive issues such as the Israel-Hamas War, ultimately contributing to efforts towards peace and resolution.