Deep Learning-Based Sentiment Analysis on Social Media Text Using Long Short-Term Memory (LSTM) - liska sipayung; Megaria Purba; Bayu Pratama; Sri Yessi Saragih Sumbayak (Universitas Budi Darma, 2026)
The large volume and informal nature of Indonesian social media text make manual sentiment analysis slow, inconsistent, and difficult to scale. This study evaluates a Long Short-Term Memory (LSTM) model for classifying public comments from X/Twitter into positive, neutral, and negative sentiment. A balanced dataset of 3,000 public Indonesian-language posts collected from January to March 2026 was manually labeled into three equal classes. Duplicate, irrelevant, advertising, and empty posts were removed; the remaining text underwent case folding, noise removal, tokenization, and padding. The data were stratified into 2,400 training and 600 testing instances. The model used a 10,000-word vocabulary, 100-token sequences, a 128-dimensional embedding, 128 LSTM units, dropout of 0.5, and a softmax output layer. On the held-out test set, the model obtained 87.00% accuracy, 86.80% precision, 86.50% recall, and 86.60% F1-score. Positive sentiment produced the strongest class-level performance, whereas neutral comments were more difficult because factual, ambiguous, and mixed expressions provide weaker affective cues. The findings show that LSTM provides a useful baseline for three-class Indonesian social media sentiment classification. However, generalization remains limited by the single-platform, topic-dependent dataset and the absence of repeated or cross-domain evaluation.