How to Automatically Detect Negative Brand Sentiment on Twitter with AI
Hugging Face is an AI company with a funny name. But its capabilities are no joke.
Hugging Face's API enables companies to build AI features into their apps. And it's used by AI giants like Amazon Web Services, Google AI, and Microsoft.
It's Inference API solution puts 10,000 AI models at your fingertips, enabling use cases like feature extraction, text classification, and conversational AI.
The models are trained in 160+ languages, too, so you can use them for natural language processing (NLP) purposes where applicable.
In fact, the company also offers AutoNLP, a way to train and deploy NLP automatically.
And, Hugging Face's experts can help you answer questions like:
- How do you optimize models for minimum latency?
- How do you optimize your production environment?
- How do you detect and mitigate bias in datasets and models?
Hugging Face got on our radar by tweeting about a novel use case for its models in marketing:
Analyzing your brand's tweets, then alert you automatically on Slack when someone tweets something negative.
Basically, you can use Hugging Face and Zapier to automate social media monitoring.
You set up a Twitter trigger in Zapier using the Hugging Face API, then run Python scripts to analyze sentiment of tweets. If sentiment registers as negative, Zapier automatically published a notification to your Slack.
It's a relatively easy way for savvy marketers to completely automate tedious, time-consuming Twitter monitoring.
And it's just one of thousands of narrow, but powerful, use cases for AI that are becoming easier and cheaper to enable as solutions like Hugging Face enter the market.
About Mike Kaput
Mike Kaput is Chief Content Officer at Marketing AI Institute and a senior consultant at PR 20/20. He writes and speaks about how marketers can understand, adopt, and pilot artificial intelligence to increase revenue and reduce costs. Full bio.