Sentiment Analysis Towards Rohingya on Instagram Using Support Vector Machine Method
Keywords:
Sentiment Analysis, Support Vector Machine, Rohingya Crisis, Social MediaAbstract
Purpose: This study investigates the application of the Support Vector Machine (SVM) method for sentiment analysis of Instagram posts related to the Rohingya crisis. This study investigates the application of the Support Vector Machine (SVM) method for sentiment analysis of Instagram posts related to the Rohingya crisis. The primary aim was to assess the effectiveness of SVM in classifying sentiments expressed in social media content, particularly focusing on the emotional complexity and nuances inherent in such sensitive topics.
Subjects and Methods: Data were collected from Instagram posts using hashtags related to the Rohingya crisis, and the SVM model was applied to classify sentiments into categories such as positive, negative, and neutral.
Results: The results demonstrated that the SVM method outperformed traditional sentiment analysis techniques, achieving higher accuracy, precision, recall, and F1 scores. This suggests that machine learning techniques, specifically SVM, offer a more accurate and reliable approach for analyzing public opinion on social media platforms, which is crucial for shaping humanitarian discourse and policy.
Conclusions: Future research should consider the integration of deep learning models for further improvements in sentiment analysis performance.
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