Researchers at the University of Central Florida have developed a sarcasm detector, Tech Xplore reports. The team taught the computer model patterns that often indicate sarcasm and combined that with teaching the program to correctly pick out cue words in sequences that were more likely to indicate sarcasm. They taught the model to do this by feeding it large data sets and then checked its accuracy. “The presence of sarcasm in text is the main hindrance in the performance of sentiment analysis,” says assistant professor of engineering Ivan Garibay. “Sarcasm isn’t always easy to identify in conversation, so you can imagine it’s pretty challenging for a computer program to do it and do it well. We developed an interpretable deep-learning model using multi-head self-attention and gated recurrent units. The multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text.”
https://techxplore.com/news/2021-05-artificial-intelligence-sarcasm-social-media.html
