PRDECT-ID: Indonesian product reviews dataset for emotions classification tasks
Rhio Sutoyo1, Said Achmad1, Andry Chowanda1
1Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta 11480 Indonesia.
Data in Brief
|September 12, 2022
Summary
This study introduces a new Indonesian emotion dataset for product reviews. This resource aids in developing machine learning models for automatic emotion recognition in e-commerce.
Area of Science:
- Natural Language Processing
- Machine Learning
- Affective Computing
Background:
- Emotion recognition is crucial for understanding communication nuances.
- Product reviews significantly influence consumer purchasing decisions.
- Automated emotion recognition in reviews requires substantial, labeled datasets, especially for under-resourced languages.
Purpose of the Study:
- To address the scarcity of labeled emotion datasets for Indonesian product reviews.
- To create a comprehensive dataset for training and evaluating emotion classification models.
- To facilitate advancements in sentiment analysis and customer feedback processing.
Main Methods:
- Collection of 5400 Indonesian product reviews across 29 diverse categories.
- Annotation of reviews with five distinct emotion labels.
- Validation of annotations by a clinical psychology expert to ensure accuracy.
Main Results:
- A novel, expert-verified dataset of Indonesian product reviews with emotion labels.
- The dataset provides a foundation for developing robust emotion classification algorithms.
- Enables research into culturally specific emotional expressions in online feedback.
Conclusions:
- The developed dataset is a valuable resource for the research community.
- It supports the creation of more accurate and culturally relevant emotion recognition systems for Indonesian e-commerce.
- Facilitates improved understanding of customer sentiment and engagement.
Keywords:
Emotions classificationNatural language processingSentiment analysisText miningText processingMore Related Videos
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