Our research found emotions expressed on X could serve as a representation of the public's general sentiments about specific non-profit organizations. These feelings had a direct impact on donation behavior.
Traditionally, researchers have relied on sentiment analysis, which categorizes messages as positive, negative, or neutral. While this method is simple and intuitive, it has limitations.
Using AI to detect specific emotions—such as joy, anger, sadness, and disgust—offers a deeper understanding of the emotional landscape in social media communications.
The transformer transfer learning model effectively detects and interprets nuanced emotions in tweets, enhancing our understanding of public sentiment and its impact on real-world actions.
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