atharva-naik/VADEC
Codes and Datasets for our SIGIR 2021 Paper: "Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach"
This project helps social scientists and public policy researchers analyze public sentiment from Twitter data, particularly around specific events or topics like COVID-19. By processing raw tweets, it provides insights into underlying emotional aspects like annoyance and optimism. Researchers can use this to understand public reaction and sentiment trends over time.
No commits in the last 6 months.
Use this if you need to analyze the specific emotional dimensions (like 'annoyed' or 'optimistic') expressed in a large collection of tweets, rather than just classifying broad emotions.
Not ideal if you're looking for a simple tool to classify tweets into general sentiment categories (positive/negative/neutral) or if your primary interest isn't in understanding the granular 'affect dimensions' of emotions.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Apr 21, 2021
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