14 papers
Sentiment Analysis of Indonesian Spotify Reviews Using Machine Learning and BiLSTM
Uliano Wilyam Purba, Andre Hadiman Rotua Parhusip, Sahid Maulana +3
This paper benchmarks classical machine learning and deep learning approaches for three-class sentiment classification of Indonesian Spotify reviews. Using 100,000 scraped reviews…
A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis
Virdio Samuel Saragih, Baruna Abirawa, Kartini Lovian Simbolon +3
The rapid growth of electronic communication has necessitated more robust systems for email classification and sentiment detection. This study presents a comparative performance an…
Benchmarking Logistic Regression, SVM, Naive Bayes, and IndoBERT Fine-Tuning for Sentiment Analysis on Indonesian Product Reviews
Nabila Zakiyah Zahra, Salwa Farhanatussaidah, Nasywa Nur Afifah +3
The exponential growth of e-commerce platforms in Indonesia has generated a massive volume of user-generated product reviews. Analyzing the sentiment of these reviews is critical f…
Benchmarking LightGBM and BiLSTM for Sentiment Analysis on Indonesian E-Commerce Reviews
Lidia Natasyah Marpaung, Vania Claresta, Iqfina Haula Halika +3
This study presents a comparative analysis between two primary approaches in Natural Language Processing (NLP): Machine Learning (ML) utilizing the PyCaret AutoML framework, and De…
Sentiment Analysis of Mobile Legends App Reviews Using Machine Learning and LSTM-Based Deep Learning Models
Vira Putri Maharani, Kharisa Harvanny, Daris Samudra +3
This paper compares Machine Learning and LSTM-based Deep Learning methods for sentiment analysis of Mobile Legends app reviews. Using a dataset of 10,000 reviews labeled as positiv…
Enhancing Game Review Sentiment Classification on Steam Platform with Attention-Based BiLSTM
Abit Ahmad Oktarian, Fadhil Fitra Wijaya, Dhafin Razaqa Luthfi +3
This paper investigates sentiment classification of Steam game reviews using an attention-based Bidirectional Long Short-Term Memory (BiLSTM) model. Using a dataset of 50,000 revie…