<![CDATA[Infrastructure development and the provision of urban amenities are important elements in supporting the quality of life of communities in Pekanbaru City. Twitter has become an alternative platform for measuring public opinion in real time. This study aims to analyze public sentiment toward the infrastructure and amenities of Pekanbaru City using the Support Vector Machine (SVM) algorithm. The dataset consists of 3,743 tweets collected through a scraping technique using the Instant Data Scraper plugin. The analysis process includes text preprocessing, namely data cleaning, case folding, tokenization, normalization, stopword removal, and stemming. The data are then transformed using the Term Frequency–Inverse Document Frequency (TF-IDF) method for term weighting. In addition, this study implements a rule-based Named Entity Recognition (NER) approach to extract location entities from textual data. The proposed SVM model is expected to classify public sentiment effectively into positive, negative, and neutral categories and identify sentiment based on the locations mentioned in the tweets.]]>
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