The researchers trained the computer model to recognize patterns that frequently indicate sarcasm, and then combined that with training the program to correctly identify cue words in sequences that were more likely to indicate sarcasm. They trained the model to do this by feeding it large amounts of data and then testing its accuracy.

Computer science researchers at the University of Central Florida, including one of Indian ancestry, have created an artificial intelligence (AI)-based sarcasm detector for social media posts.

Sarcasm has been a major barrier to improving sentiment analysis accuracy, particularly on social media, because sarcasm relies heavily on vocal tones, facial expressions, and gestures that cannot be represented in text.

While AI refers to logical data analysis and response, sentiment analysis is similar to correctly identifying emotional communication on social media. “The presence of sarcasm in the text is the main hindrance in the performance of sentiment analysis,” says Ivan Garibay, Assistant Professor of engineering at the University of Central Florida's Complex Adaptive Systems Lab (CASL).

Sarcasm isn't always easy to spot in conversation, so you can imagine how difficult it would be for a computer program to do it well. Garibay wrote in the journal Entropy, "We developed an interpretable deep learning model using multi-head self-attention and gated recurrent units."

The researchers trained the computer model to recognize patterns that frequently indicate sarcasm, and then combined that with training the program to correctly identify cue words in sequences that were more likely to indicate sarcasm. They trained the model to do this by feeding it large amounts of data and then testing its accuracy.

Ramya Akula, a doctoral student in computer science, was part of the team. “In a face-to-face conversation, sarcasm can be easily identified using facial expressions, gestures, and the speaker's tone,” Akula said.

“Detecting sarcasm in textual communication is difficult because none of these cues are readily available. Sarcasm detection in online communications from social networking platforms is much more difficult, especially with the explosion of internet usage,” she added.

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