dvamossy/EmTract
Package for extracting emotions from social media text. Tailored for financial data.
EmTract helps financial analysts, traders, and market researchers understand public sentiment by extracting emotions from social media text, especially from platforms like StockTwits and Twitter. You input social media posts, and it outputs probabilities for seven emotions (neutral, happy, sad, anger, disgust, surprise, fear), along with the most likely emotion label. This allows you to gauge market mood or public reaction to financial news.
No commits in the last 6 months.
Use this if you need to analyze the emotional tone of social media discussions related to financial markets, companies, or assets.
Not ideal if your primary need is to analyze sentiment from general social media text outside of a financial context, as it's specifically tuned for financial language.
Stars
21
Forks
3
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 15, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/dvamossy/EmTract"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
chatopera/efaqa-corpus-zh
❤️Emotional First Aid Dataset, 心理咨询问答、聊天机器人语料库
ikegami-yukino/pymlask
Emotion analyzer for Japanese text
declare-lab/conv-emotion
This repo contains implementation of different architectures for emotion recognition in conversations.
sarnthil/unify-emotion-datasets
A Survey and Experiments on Annotated Corpora for Emotion Classification in Text
kanchitank/Text-Emotion-Analysis
Automate detection of different emotions from paragraphs and predict overall emotion.