  The stakeholders' needs in sentiment analysis for various issues, whether
positive or negative, are speed and accuracy. One new challenge in sentiment
analysis tasks is the limited training data, which often leads to suboptimal
machine learning models and poor performance on test data. This paper discusses
the problem of text classification based on limited training data (300 to 600
samples) into three classes: positive, negative, and neutral. A benchmark
dataset is provided for training and testing data on the issue of Kaesang
Pangarep's appointment as Chairman of PSI. External data for aggregation and
augmentation purposes are provided, consisting of two datasets: the topic of
Covid Vaccination sentiment and an open topic. The official score used is the
F1-score, which balances precision and recall among the three classes,
positive, negative, and neutral. A baseline score is provided as a reference
for researchers for unoptimized classification methods. The optimized score is
provided as a reference for the target score to be achieved by any proposed
method. Both scoring (baseline and optimized) use the SVM method, which is
widely reported as the state-of-the-art in conventional machine learning
methods. The F1-scores achieved by the baseline and optimized methods are
40.83% and 51.28%, respectively.
