# Social Media Monitoring

A sentiment analysis model based on DistilBERT that classifies English texts into positive, negative, or neutral sentiments.

Chinese sentiment classification model fine-tuned based on xlm-roberta-large-xnli, capable of recognizing 8 emotion labels

A multilingual sentiment analysis model fine-tuned based on DistilBERT, supporting 21 languages, suitable for various scenarios such as social media and customer feedback analysis.

A multilingual fine-tuned version based on RoBERTa, specifically optimized for sentiment classification tasks in Hungarian, Slovak, Polish, and Czech languages.

A German emotion classification model fine-tuned based on RoBERTa, capable of classifying text into six emotion categories: anger, fear, disgust, sadness, happiness, and none of the above.

This is a model fine-tuned based on the Bert architecture, specifically designed to analyze the sentiment tendency of text messages, capable of distinguishing between positive and negative sentiments.

This model is used to identify and classify propaganda techniques in text, helping users detect potential propaganda content.

This is a model for sentiment analysis of Korean text, capable of determining the emotional tendency (positive/negative) of the text.

A model for detecting emotions in text, capable of identifying positive, negative, or neutral sentiments.

BERT model optimized for Swedish fear sentiment analysis, capable of identifying fear/anxiety intensity in text