Arpanghoshal

EmoRoBERTa

arpanghoshal

92

EmoRoBERTa is a model trained by arpanghoshal on the GoEmotions dataset, which contains 58,000 Reddit comments labeled with 28 different emotions. The model is based on the RoBERTa architecture and has been fine-tuned for emotion classification. Similar models include: roberta-base-go_emotions - A RoBERTa-base model trained on the GoEmotions dataset. distilroberta-finetuned-financial-news-sentiment-analysis - A DistilRoBERTa model fine-tuned for financial news sentiment analysis. twitter-roberta-base-sentiment - A RoBERTa-base model trained on tweets and fine-tuned for sentiment analysis. distilbert-base-uncased-emotion - A DistilBERT model fine-tuned for emotion classification on Twitter data. Model inputs and outputs Inputs Text data, such as sentences or short paragraphs, that the model will analyze for emotional content. Outputs A list of emotion labels and their corresponding probabilities for the input text. The 28 emotion labels are: admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, and surprise. Capabilities The EmoRoBERTa model can be used to analyze the emotional content of text, identifying the predominant emotions expressed. This can be useful for a variety of applications, such as customer service sentiment analysis, social media monitoring, or literary/creative analysis. What can I use it for? The EmoRoBERTa model could be used in applications that require understanding the emotional state of users or analyzing the emotional tone of written content. For example, a company could use it to monitor customer feedback and identify areas of concern or positive sentiment. Writers could use it to better understand the emotional arc of their stories. Researchers could use it to study the expression of emotions in online discourse. Things to try Some interesting things to try with the EmoRoBERTa model include: Analyzing the emotional content of user reviews or social media posts to understand trends and sentiment. Comparing the emotional profiles of different genres of writing or content creators. Experimenting with different thresholds for emotion classification to see how it affects the model's performance. Combining the emotion predictions with other NLP tasks, such as topic modeling or named entity recognition, to gain deeper insights. Overall, the EmoRoBERTa model provides a powerful tool for understanding the emotional dimensions of text data, with a wide range of potential applications.

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