<![CDATA[On the Google Play Store platform, there is a review section for each app containing users' opinions and experiences when using an app. These user reviews can be used as a basis for evaluating the quality of an app’s service; however, the large number of reviews makes it difficult for developers to analyze them manually. Threads, a social media app that provides a means of communication and entertainment created by Meta, has received many reviews from its users. This study analyzes user sentiment toward the Threads app through review classification to identify positive and negative opinions. A total of 1,808 user review data for the Threads app was collected as research data, consisting of 961 positive data and 847 negative data. Sentiment labeling will be done using the AI Copilot tool. The data will go through several stages, including data selection, data cleaning, data normalization, and word weighting using the TF-IDF method before performing data mining using the Support Vector Machine (SVM) algorithm. The test results showed that the model built was able to classify sentiment with an accuracy rate of 93.03% on an 80:20 train-test data split using the rbf kernel. In the Word Cloud, positive sentiments were dominated by words related to users’ appreciation for the Threads app services, while negative sentiments were dominated by users’ complaints about the suspension system and features in the Threads app. These research results indicate that the method used is capable of identifying user opinions and can be used as a basis for evaluating improvements in app service quality.]]>
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