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[ARTICLE · art-100630] src=discuss.huggingface.co ↗ pub= topic=natural-language-processing verified=true sentiment=· neutral

Which open-source models work well for sentiment analysis of social media posts?

Cardiff University's NLP group and other open-source developers offer free sentiment analysis models for social media posts, including cardiffnlp/twitter-roberta-base-sentiment-latest, finiteautomata/bertweet-base-sentiment-analysis, and nlptown/bert-base-multilingual-uncased-sentiment for multilingual coverage. Experts recommend testing these models on a hand-labeled validation set from your own platform to account for sarcasm, slang, and emojis.

read1 min views1 publishedAug 18, 2026
Which open-source models work well for sentiment analysis of social media posts?
Image: Discuss (auto-discovered)

Which free AI models can I use to find out whether social media posts are positive, negative, or neutral?

For a simple positive / negative / neutral baseline, I’d start with a model already fine-tuned for social text rather than a general chat model. Good options to try on Hugging Face are:

- cardiffnlp/twitter-roberta-base-sentiment-latest
- finiteautomata/bertweet-base-sentiment-analysis
- nlptown/bert-base-multilingual-uncased-sentiment if you need multilingual coverage

If the posts are short and noisy, test on your own sample first. Social posts often contain sarcasm, slang, emojis, and context that basic sentiment labels miss. For production use, I’d keep a small hand-labeled validation set from your actual platform and compare models against that instead of trusting the model card alone.
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