Pranav-Goel/Neural_Emotion_Intensity_Prediction

The code for our proposed neural models which give state-of-the-art performance for emotion intensity detection in tweets.

29
/ 100
Experimental

This project helps social media analysts, brand managers, and researchers understand the intensity of emotions expressed in tweets. You provide a collection of tweets, and it outputs a score for how strongly each tweet expresses specific emotions like joy, sadness, or anger. This allows you to track public sentiment or reaction to events and campaigns more precisely.

No commits in the last 6 months.

Use this if you need to quantify the strength of emotions in Twitter data for sentiment analysis, market research, or social science studies.

Not ideal if you need to analyze emotion in long-form text, general web content, or platforms other than Twitter.

social-media-analytics sentiment-analysis market-research public-opinion brand-monitoring
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 8 / 25
Community 15 / 25

How are scores calculated?

Stars

20

Forks

5

Language

Jupyter Notebook

License

Last pushed

Oct 12, 2018

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/nlp/Pranav-Goel/Neural_Emotion_Intensity_Prediction"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.