Sarcasm is notoriously tricky to convey through text, and the subtle changes in tone that convey sarcasm often confuse computer algorithms as well, limiting virtual assistants and content analysis tools.
So researchers have now developed a multimodal algorithm for improved sarcasm detection that examines multiple aspects of audio recordings for increased accuracy. They used two complementary approaches -- sentiment analysis using text and emotion recognition using audio -- for a more complete picture.
Traditional sarcasm detection algorithms often rely on a single parameter to produce their results, which is the main reason they often fall short. Gao, Nayak, and Coler instead used two complementary approaches -- sentiment analysis using text and emotion recognition using audio -- for a more complete picture.
"There are a range of expressions and gestures people use to highlight sarcastic elements in speech," said Gao."These need to be better integrated into our project. In addition, we would like to include more languages and adopt developing sarcasm recognition techniques."
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