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Enrich Socialgist News with Datastreamer Emotion Detection Classifier
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About Socialgist News
Drawing from over 1,000 Chinese news sources and over 25,000 English news sources, Socialgist provides a comprehensive overview of current events, editorial opinions, and journalistic analysis. This dataset is invaluable for understanding media narratives, tracking news cycles, and analyzing the impact of current events on public discourse and sentiment across various regions and topics.
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 Socialgist News with Datastreamer Emotion Detection Classifier with a Datstreamer Pipeline.
Step 1
Start your Pipeline with Socialgist News
Web data serves as the foundational input for any data pipeline. Pipelines can be powered by diverse data sources, including datasets from our partner ecosystem, proprietary internal systems, or any externally accessible web data.
Step 2
Add Datastreamer Emotion Detection Classifier to enrich
Make your web data work harder. With Datastreamer, you can enrich, filter, join, structure, store, or search data effortlessly using hundreds of out-of-the-box operations.
Step 3
That's it! You have just connected Socialgist News and Datastreamer Emotion Detection Classifier
Web data, unlocked. Datastreamer empowers you to expand your Pipelines as needed while removing friction from your operations.