Developing a better understanding of customer feedback on social media platforms is crucial for brands’ success, but it is incredibly labour-intensive. However, it may have just gotten easier thanks to new research by computer science researchers at the University of Central Florida who has developed a “sarcasm detector”.
A UCF team developed a technique that accurately detects sarcasm in a social media text.
The team effectively taught the computer model to detect patterns that often indicate sarcasm by correctly picking out cue words in sequences that were more likely to indicate sarcasm. They did so by feeding it large data sets and then verifying the accuracy.
“The presence of sarcasm in the text is the main hindrance in the performance of sentiment analysis,” says Assistant Professor of engineering Ivan Garibay.
“Sarcasm is not 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 classify the input text better,” he added.