Sentiment Analysis of Animated Film “JUMBO” on
Twitter Using Random Forest and Semi-Supervised
Learning

Sentiment Analysis of Animated Film “JUMBO” on
Twitter Using Random Forest and Semi-Supervised
Learning

Sentiment analysis, Twitter, Random Forest, Semi-supervised learning, Film review classification

This study investigates public sentiment toward the Indonesian animated film "JUMBO" using Twitter data and a
semi-supervised machine learning approach. Two thousand fifty tweets were collected and preprocessed to
remove noise, standardize text, and extract meaningful features. Data was collected between April 6, 2025, and
May 13, 2025, following the film's official release on March 31, 2025, coinciding with its peak public discussion
window. A semi-supervised learning strategy was applied, where 532 tweets were manually labelled into positive,
neutral, or negative sentiment categories, mitigating the extensive need for labelled data. To address the class
imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was employed. The labelled data were
then used to train a Random Forest classifier, achieving an accuracy of 90% and balanced F1 scores across all
classes. The model was subsequently applied to classify the remaining unlabeled tweets, which revealed a
dominant proportion of positive sentiments toward the film. These results obtain strong public approval of
"JUMBO" and demonstrate the effectiveness of combining machine learning with semi-supervised techniques for
sentiment analysis, particularly in the context of local cultural products. This research can be an initial stage in a
broader roadmap for analyzing the success factors of Indonesian animated films through AI-driven approach

Eko Rahmat Slamet Hidayat Saputra1
, Arvin Claudy Frobenius2

https://ijicom.respati.ac.id/index.php/ijicom/article/view/79/97

International Journal of Informatics and Computation (IJICOM)

2025

Fajar bagus W

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