Sentiment Analysis of Instagram User Comments related to the Inauguration of Mr. Prabowo Subianto as President of the Republic of Indonesia Using Natural Language Processing

This study employs Natural Language Processing (NLP) techniques to analyze sentiments expressed in Instagram comments about Prabowo Subianto's inauguration as Indonesia's president. The dataset comprises a rich collection of user-generated comments, meticulously preprocessed with the Sastrawi stemmer tailored for Indonesian. This preprocessing stage includes rigorous text cleaning, stemming, and stopword removal, ensuring that the analysis is based on the most relevant linguistic elements. To accurately classify the sentiment of these comments as positive or negative, a logistic regression model has been trained. The model leverages TF-IDF (Term Frequency-Inverse Document Frequency) for effective feature extraction, enhancing the precision of the analysis. With promising results, particularly in identifying uplifting remarks that celebrate the new president's ascendance, this study underscores the essential role of natural language processing in unraveling public sentiment surrounding pivotal political events. The findings of this research not only shed light on the intricate tapestry of public opinion but also pave the way for future sentiment analysis endeavors within the vibrant landscape of Indonesian social media. The model demonstrates robust accuracy, illustrating its effectiveness in interpreting the nuanced sentiments of digital discourse surrounding significant political milestones.