jon-chun/sentimentarcs_notebooks
SentimentArcs: a large ensemble of dozens of sentiment analysis models to analyze emotion in text over time
This tool helps researchers, analysts, and literary experts understand how emotions evolve within long texts or collections of shorter texts over time. It takes any sequence of text, applies an array of sentiment analysis models, and outputs a visual representation of emotional arcs, highlighting key shifts. Users can then extract specific text segments that correspond to significant emotional changes for deeper human analysis.
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Use this if you need to identify and analyze emotional trends and specific pivotal moments within narrative texts, social media feeds, financial reports, or any other time-sequenced textual data.
Not ideal if you only need a single, overall sentiment score for a short piece of text or if your text data lacks a meaningful sequential order.
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42
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Jupyter Notebook
License
MIT
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Last pushed
Mar 23, 2023
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