kanchitank/Text-Emotion-Analysis

Automate detection of different emotions from paragraphs and predict overall emotion.

43
/ 100
Emerging

This project helps social media managers, market researchers, and customer service teams automatically understand the emotional tone of text. You input English sentences or paragraphs, and it identifies specific emotions like happy, sad, or angry within the text, then predicts the overall emotion. It's designed for anyone needing to quickly gauge public sentiment or individual emotional responses from text data.

No commits in the last 6 months.

Use this if you need to quickly assess the emotional content of a large volume of English text, such as tweets, customer feedback, or written reviews.

Not ideal if you require highly nuanced or context-specific emotion detection beyond the five main categories (Neutral, Happy, Sad, Love, Anger) provided.

social-media-analysis customer-feedback sentiment-analysis market-research content-moderation
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 19 / 25

How are scores calculated?

Stars

60

Forks

18

Language

Jupyter Notebook

License

MIT

Last pushed

Apr 19, 2021

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/nlp/kanchitank/Text-Emotion-Analysis"

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