Sentiment Analysis of Threads Application Users Using Support Vector Machine Method

Nurul Alam, Mahdi Mahdi, Musta’inul Abdi

Abstract


The Threads app is a social media platform launched by Instagram, where users can communicate, connect, and discuss various topics. With the rapid growth of social media users in Indonesia, Threads has gained attention as a new platform with over 100 million users. Generally, Threads features an interface and algorithm similar to Twitter, allowing users to share text-based posts, photos, and videos. Given the vast number of reviews available on the Google Play Store, manual analysis is impractical, hence sentiment analysis is conducted to classify these reviews. This study aims to analyze user sentiment toward the Threads app using the Support Vector Machine (SVM) method on user reviews in the Google Play Store, focusing on Indonesian-language reviews to classify opinions into positive, neutral, and negative. A total of 200 review samples were analyzed, yielding 104 positive reviews, 75 neutral reviews, and 21 negative reviews. The sentiment classification results using the SVM method achieved an accuracy of 80%. This accuracy indicates that the SVM method effectively classifies user sentiments toward the Threads app. The results show that Threads receives a positive response from the majority of users, suggesting that the app is well-received and acceptable as a new social media platform.

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