twitter-roberta-base-sentiment
Maintainer: cardiffnlp
247
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| 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 model is a RoBERTa-base model trained on ~58M tweets and fine-tuned for sentiment analysis using the TweetEval benchmark. This model is suitable for analyzing the sentiment of English text, particularly tweets and other social media content. It can classify text as either negative, neutral, or positive.
Compared to similar models like twitter-xlm-roberta-base-sentiment, which is a multilingual model, the twitter-roberta-base-sentiment is specialized for English. The sentiment-roberta-large-english model is another English-focused sentiment analysis model, but it is based on the larger RoBERTa-large architecture.
Model inputs and outputs
Inputs
- Text: The model takes in English-language text, such as tweets, reviews, or other social media posts.
Outputs
- Sentiment score: The model outputs a sentiment score that classifies the input text as either negative (0), neutral (1), or positive (2).
Capabilities
The twitter-roberta-base-sentiment model can be used to perform reliable sentiment analysis on a variety of English-language text. It has been trained and evaluated on a wide range of datasets, including reviews, tweets, and other social media content, and has been shown to outperform models trained on a single dataset.
What can I use it for?
This model could be useful for a variety of applications that involve analyzing the sentiment of text, such as:
- Monitoring social media sentiment around a brand, product, or event
- Analyzing customer feedback and reviews to gain insights into customer satisfaction
- Identifying and tracking sentiment trends in online discussions or news coverage
Things to try
One interesting thing to try with this model is to compare its performance on different types of English-language text, such as formal writing versus informal social media posts. You could also experiment with using the model's output scores to track sentiment trends over time or to identify the most polarizing topics in a dataset.
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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