Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning
Abstract Sentiment analysis aims to classify text based on the opinion or mentality expressed in a situation, which can be positive, negative, or neutral. Therefore, in the world, a lot of opinions are available on various social media sites, which must be gathered and analyzed to assess the general...
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Nature Portfolio
2024-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-63367-3 |
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| author | Ashagrew Liyih Shegaw Anagaw Minichel Yibeyin Yitayal Tehone |
| author_facet | Ashagrew Liyih Shegaw Anagaw Minichel Yibeyin Yitayal Tehone |
| author_sort | Ashagrew Liyih |
| collection | DOAJ |
| description | Abstract Sentiment analysis aims to classify text based on the opinion or mentality expressed in a situation, which can be positive, negative, or neutral. Therefore, in the world, a lot of opinions are available on various social media sites, which must be gathered and analyzed to assess the general public’s opinion. Finding and monitoring comments, as well as manually extracting the information contained in them, is a difficult task due to the vast diversity of ideas on YouTube. Identifying public opinion on war topics is crucial for offering insights to opposing sides based on popular opinion and emotions about the ongoing war. To address the gap, we build a model on YouTube comment sentiment analysis of the Hamas-Israel war to determine public opinion. In this study, we address the gaps by developing a deep learning-based approach for sentiment analysis. We have collected 24,360 comments from popular YouTube News Channels including BBC, WION, Aljazeera, and others about the Hamas-Israel War using YouTube API and Google spreadsheet and labeled them by linguistic experts into three classes: positive, negative, and neutral. Then, textual comments were preprocessed using natural language processing (NLP) techniques, and features were extracted using Word2vec, FastText, and GloVe. Moreover, we have used the SMOTE data balancing technique and used different data splits, but the 80/20 train-test split ratio has the highest accuracy. For classification model building, commonly used classification algorithms LSTM, Bi-LSTM, GRU, and Hybrid of CNN and Bi-LSTM were applied, and their performance is compared. As a result, the Hybrid of CNN and Bi-LSTM with Word2vec achieved the highest performance with 95.73% accuracy for comments classifications. |
| format | Article |
| id | doaj-art-f300b7537dfa46dba700366f452b9587 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f300b7537dfa46dba700366f452b95872025-02-09T12:37:57ZengNature PortfolioScientific Reports2045-23222024-06-011411910.1038/s41598-024-63367-3Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learningAshagrew Liyih0Shegaw Anagaw1Minichel Yibeyin2Yitayal Tehone3Department of Software Engineering, Debre Markos UniversityDepartment of Business, University of Southeastern NorwayDepartment of Information Technology, Debre Markos UniversityDepartment of Software Engineering, Debre Markos UniversityAbstract Sentiment analysis aims to classify text based on the opinion or mentality expressed in a situation, which can be positive, negative, or neutral. Therefore, in the world, a lot of opinions are available on various social media sites, which must be gathered and analyzed to assess the general public’s opinion. Finding and monitoring comments, as well as manually extracting the information contained in them, is a difficult task due to the vast diversity of ideas on YouTube. Identifying public opinion on war topics is crucial for offering insights to opposing sides based on popular opinion and emotions about the ongoing war. To address the gap, we build a model on YouTube comment sentiment analysis of the Hamas-Israel war to determine public opinion. In this study, we address the gaps by developing a deep learning-based approach for sentiment analysis. We have collected 24,360 comments from popular YouTube News Channels including BBC, WION, Aljazeera, and others about the Hamas-Israel War using YouTube API and Google spreadsheet and labeled them by linguistic experts into three classes: positive, negative, and neutral. Then, textual comments were preprocessed using natural language processing (NLP) techniques, and features were extracted using Word2vec, FastText, and GloVe. Moreover, we have used the SMOTE data balancing technique and used different data splits, but the 80/20 train-test split ratio has the highest accuracy. For classification model building, commonly used classification algorithms LSTM, Bi-LSTM, GRU, and Hybrid of CNN and Bi-LSTM were applied, and their performance is compared. As a result, the Hybrid of CNN and Bi-LSTM with Word2vec achieved the highest performance with 95.73% accuracy for comments classifications.https://doi.org/10.1038/s41598-024-63367-3Deep learning approachRecurrent neural networkSentiment analysisWord2vecFastTextGloVe |
| spellingShingle | Ashagrew Liyih Shegaw Anagaw Minichel Yibeyin Yitayal Tehone Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning Scientific Reports Deep learning approach Recurrent neural network Sentiment analysis Word2vec FastText GloVe |
| title | Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning |
| title_full | Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning |
| title_fullStr | Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning |
| title_full_unstemmed | Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning |
| title_short | Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning |
| title_sort | sentiment analysis of the hamas israel war on youtube comments using deep learning |
| topic | Deep learning approach Recurrent neural network Sentiment analysis Word2vec FastText GloVe |
| url | https://doi.org/10.1038/s41598-024-63367-3 |
| work_keys_str_mv | AT ashagrewliyih sentimentanalysisofthehamasisraelwaronyoutubecommentsusingdeeplearning AT shegawanagaw sentimentanalysisofthehamasisraelwaronyoutubecommentsusingdeeplearning AT minichelyibeyin sentimentanalysisofthehamasisraelwaronyoutubecommentsusingdeeplearning AT yitayaltehone sentimentanalysisofthehamasisraelwaronyoutubecommentsusingdeeplearning |