# Indonesian Sentiment Analysis

An Indonesian product review sentiment classification model fine-tuned based on IndoBERT, supporting positive and negative sentiment classification.

This is a BERT-based sentiment analysis model for Indonesian language, capable of classifying text into positive or negative sentiments.

EmoSense-ID is a model designed to identify and analyze emotions in Indonesian text based on Plutchik's eight basic emotions.

This model is an optimized sentiment classification model based on IndoRoBERTa, specifically designed for multi-class sentiment and emotion classification in Indonesian Twitter texts.

A sentiment analysis model based on the IndoBERT pre-trained model for predicting six types of emotions in Indonesian sentences.

A sentiment classifier trained on the IndoBERT base model, supporting sentiment analysis for Indonesian text

An Indonesian emotion classifier trained on the Indo-roberta model, fine-tuned on the IndoNLU EmoT dataset for sentiment analysis tasks.

An Indonesian sentiment analysis model based on the RoBERTa architecture, capable of identifying emotional tendencies in text.

A RoBERTa-based Indonesian sentiment text classification model, fine-tuned on the SmSA dataset with an accuracy of 94.36%

This is an Indonesian sentiment analysis model based on the BERT architecture, fine-tuned on the SMSA dataset with an accuracy of 93.73%

A sentiment analysis model fine-tuned on the Indonesian RoBERTa model, achieving 92.6% accuracy on the indonlu dataset

A RoBERTa-based sentiment text classification model for Indonesian language, fine-tuned on the SmSA dataset, used to analyze the sentiment tendencies of Indonesian reviews and evaluations.

This model is a text classification model fine-tuned on the indonlu dataset based on indobert-base-uncased, specifically designed for Indonesian language sentiment analysis tasks.

This is an Indonesian sentiment analysis model based on the RoBERTa architecture, fine-tuned on the indonlu dataset, specifically designed for sentiment classification tasks in Indonesian SMS texts.