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Lazada Xlm Roberta Sentiment
L
Lazada Xlm Roberta Sentiment
Developed by The-Thesis-Gods
A multilingual sentiment analysis model based on XLM-RoBERTa, supporting AutoTrain for text classification tasks.
Downloads 43
Release Time : 4/17/2025
Model Overview
This model is a multilingual sentiment analysis model specifically optimized for Twitter text, capable of identifying emotional tendencies in text.
Model Features
Multilingual Support
Based on the XLM-RoBERTa architecture, it can handle sentiment analysis for texts in multiple languages.
Twitter Text Optimization
Specially trained and optimized for Twitter text, making it suitable for social media sentiment analysis.
High Performance
Outstanding performance on validation sets with an F1 score of up to 0.987 and an accuracy of 0.979.
Model Capabilities
Text Classification
Sentiment Analysis
Multilingual Text Processing
Use Cases
Social Media Analysis
Twitter Sentiment Monitoring
Analyze the sentiment tendencies of Twitter users towards specific topics or brands.
Accurately identifies positive, negative, and neutral sentiments with an F1 score of 0.987.
Market Research
Product Feedback Analysis
Extract sentiment tendencies from user reviews to assess product satisfaction.
High-accuracy sentiment classification helps quickly identify user preferences.
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