twitter-roberta-base-sentiment-latest
Maintainer: cardiffnlp
436
🛠️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The twitter-roberta-base-sentiment-latest model is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021 and fine-tuned for sentiment analysis using the TweetEval benchmark. This model builds on the original Twitter-based RoBERTa model and the TweetEval benchmark. The model is suitable for English language sentiment analysis and was created by the cardiffnlp team.
Model inputs and outputs
The twitter-roberta-base-sentiment-latest model takes in English text and outputs sentiment labels of 0 (Negative), 1 (Neutral), or 2 (Positive), along with confidence scores for each label. The model can be used for both simple sentiment analysis tasks as well as more advanced text classification projects.
Inputs
- English text, such as tweets, reviews, or other short passages
Outputs
- Sentiment label (0, 1, or 2)
- Confidence score for each sentiment label
Capabilities
The twitter-roberta-base-sentiment-latest model can accurately classify the sentiment of short English text. It excels at analyzing the emotional tone of tweets, social media posts, and other informal online content. The model was trained on a large, up-to-date dataset of tweets, giving it strong performance on the nuanced language used in many online conversations.
What can I use it for?
This sentiment analysis model can be used for a variety of applications, such as:
- Monitoring brand reputation and customer sentiment on social media
- Detecting emotional reactions to news, events, or products
- Analyzing customer feedback and reviews to inform business decisions
- Powering chatbots and virtual assistants with natural language understanding
Things to try
To get started with the twitter-roberta-base-sentiment-latest model, you can try experimenting with different types of text inputs, such as tweets, customer reviews, or news articles. See how the model performs on short, informal language versus more formal written content. You can also try combining this sentiment model with other NLP tasks, like topic modeling or named entity recognition, to gain deeper insights from your data.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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