fhamborg/NewsMTSC
Target-dependent sentiment classification in news articles reporting on political events. Includes a high-quality data set of over 11k sentences and a state-of-the-art classification model.
This project offers a high-quality dataset and a ready-to-use model for analyzing sentiment in news articles, specifically focusing on how different entities (targets) are portrayed. It takes news sentences as input and outputs the sentiment directed at specific subjects mentioned within them. This is useful for political scientists, media analysts, and researchers tracking public opinion or media bias.
156 stars. No commits in the last 6 months.
Use this if you need to classify sentiment in news articles about policy issues, especially when you want to understand the sentiment towards specific people, organizations, or concepts mentioned in the text.
Not ideal if your primary goal is general sentiment classification across various text types, as this is specifically tuned for news articles and target-dependent sentiment.
Stars
156
Forks
20
Language
Python
License
—
Category
Last pushed
Jul 18, 2025
Commits (30d)
0
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