@DravidianLangTech 2026: A Comparative Study of TF-IDF SVM and XLM-RoBERTa for Political Multiclass Text Classification.

A Comparison of Classical Machine Learning and IndoBERT on Sentiment Analysis of Danantara Program in X

The rapid growth of social media has made it a primary channel for the public to express opinions on national strategic economic policies, including the establishment of the Danantara entity. This study aims to map public sentiment on Platform X and compare the performance of classical frequency-based architectures wit...

Performance Evaluation of Word2Vec and FastText Embeddings in a CNN-BiLSTM Model for Sentiment Classification of the LPDP Alumni Controversy

This study aims to analyze public sentiment toward the LPDP alumni controversy on social media using a deep learning approach. The research data consist of YouTube user comments related to the LPDP issue, which were processed through text preprocessing and automatically labeled using IndoBERT into three sentiment class...

LSTM-Based Classification of Indonesian Regional Song Lyrics by Language

This study successfully proposes a Long Short-Term Memory (LSTM)-based model for automatic classification of Indonesian regional song lyrics by language, demonstrating that LSTM effectively captures sequential linguistic patterns and contextual relationships within regional languages.

Meningkatkan Deteksi Ujaran Kebencian dan Bahasa

EXPERIMENTAL COMPARISON OF TEXT VECTORIZATION METHODS FOR SENTIMENT ANALYSIS TASK

The paper presents a comparative analysis of the effectiveness of various text vectorization methods for the task of Sentiment Analysis of Russian-language reviews. The study covers classical frequency-based approaches (TF IDF, n-grams), statistical models (Word2Vec, FastText), and a contextual method based on the pre-...

Comparative Analysis of Language Models for Sentiment Classification

Comparing and analysing the performance of several machine learning algorithms on fine-grained sentiment classification problems to examine their suitability and shortcomings for use as models in sentiment analysis suggests large language models perform significantly worse on the 28-class classification task in zero-sh...