Computer science researchers at the University of Central Florida have developed an artificial intelligence (AI)-based sarcasm detector for posts on social media platforms.
Sarcasm has been a major hurdle to increasing the accuracy of sentiment analysis, especially on social media, since sarcasm relies heavily on vocal tones, facial expressions and gestures that cannot be represented in the text.
While AI refers to logical data analysis and response, sentiment analysis is akin 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 from Complex Adaptive Systems Lab (CASL) at the University of Central Florida.
Sarcasm isn’t always easy to identify in conversation, so you can imagine it’s pretty challenging for a computer programme to do it and do it well.
“We developed an interpretable deep learning model using multi-head self-attention and gated recurrent units”, Garibay said in a paper published in the journal Entropy.
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The team taught the computer model to find patterns that often indicate sarcasm and combined that with teaching the programme 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 team included computer science doctoral student Ramya Akula.
“In face-to-face conversation, sarcasm can be identified effortlessly using facial expressions, gestures, and tone of the speaker”, Akula said.
“Detecting sarcasm in textual communication is not a trivial task as none of these cues is readily available. Especially with the explosion of internet usage, sarcasm detection in online communications from social networking platforms is much more challenging”, she added.
(With inputs from agencies)
