Sentiment Analysis of Motivational Content on Social Media Using Support Vector Machine

Nadiah Zakira, Salahuddin Salahuddin

Abstract


Social media has become a primary platform for sharing information, including motivational content that holds significant potential to influence user mindset and behavior. Motivation, whether intrinsic or from one's environment, plays a crucial role in driving an individual's daily activities. Therefore, sentiment analysis of motivational content on social media is relevant for understanding its impact on users' psychological states, such as boosting life spirit or productivity. This research focuses on performing sentiment analysis on motivational sentences from social media using the Support Vector Machine (SVM) method. SVM was chosen for its proven capability in text classification with high accuracy, even on complex and high-dimensional data. The research process included data collection (crawling), text preprocessing using NLP techniques, sentiment labeling, and the training and testing of the classification model using the SVM algorithm. The model was evaluated using a Confusion Matrix to measure accuracy, precision, recall, and F1-score. The test results demonstrate that the built sentiment analysis system achieved an accuracy of 96.62%. Furthermore, the obtained F1-scores were 97.19% for the positive label, 96.84% for neutral, and 95.04% for negative, indicating that the SVM method is highly effective in classifying the sentiment of motivational content. Thus, this study successfully proves that SVM is a valid and reliable method for analyzing the emotional impact of motivational content on social media.

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