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Enrich Bright Data YouTube with Datastreamer Emotion Detection Classifier
Top companies trust Datastreamer to integrate, enrich, join, and apply their web data needs.
About Bright Data YouTube
Collect and extract YouTube video and profile search results using keywords, and YouTube comments using URLs. Further refine the video and profile searches by start date and end date using additional parameters.
About Datastreamer Emotion Detection Classifier
Detect emotion within content, such as fear, happiness, sadness, disgust, etc. It is able to process content in over 200+ languages. This classifier can instantly consume the content within a pipeline, optimize the content for speed and cost efficiency, and pass into LLM systems. Within the classifier, the LLM response is restructured, the post is augmented with the new metadata, and continues in the pipeline.
Quickly enrich Bright Data YouTube with Datastreamer Emotion Detection Classifier with a Datstreamer Pipeline.
Step 1
Start your Pipeline with Bright Data YouTube
Web data is the foundation of any pipeline. You can leverage a wide range of data sources to power your pipelines—whether it's data from our partner network, your internal systems, or any publicly available web data.
Step 2
Add Datastreamer Emotion Detection Classifier to enrich
Datastreamer lets you accelerate your data usage by applying operations like structuring, enriching, joining, and filtering—choose from hundreds of prebuilt, plug-and-play options.
Step 3
That's it! You have just connected Bright Data YouTube and Datastreamer Emotion Detection Classifier
Supercharge your data workflows with Datastreamer. Add flexibility to your Pipelines and put an end to the common bottlenecks in handling web data.