Application Of TF-IDF And Word2vec For Feature Extraction In Sentiment Analysis Of Free Nutritious Food Policies

Abstract

The free nutritious meal policy has become a hot topic of discussion among the public because it is related to improving health and education quality. However, its implementation has given rise to a variety of pros and cons that need to be analyzed systematically. This study aims to analyze sentiment toward the policy by utilizing Term Frequency–Inverse Document Frequency (TF-IDF) and Word2Vec as feature extraction methods on public review data obtained from social media X. After undergoing preprocessing and automatic labeling, the data was classified into positive and negative sentiments using the Support Vector Machine (SVM) algorithm. The analysis results  how that the sentiment data is unbalanced, with the positive class dominating at 75% and the negative class at 25%. In model testing, TF-IDF achieved an accuracy of 81%, while Word2Vec achieved an accuracy of 80%. This difference shows that TF-IDF is more stable in handling short and informal texts, while Word2Vec still has the potential to capture the semantic context between words. This research opens up opportunities for further research, it is recommended to balance the data between classes and combine the TF-IDF and Word2Vec methods, or use a deep learning approach such as BERT to obtain more accurate results and capture deeper semantic context.