Researchers develop artificial intelligence capable of detecting sarcasm in social media, Technology News
Understanding and responding to customer feedback on social media platforms is crucial for brands, and it just might have just gotten a little easier thanks to new research from computer scientists at the University of Central Florida. who have developed a sarcasm detector.
Social media has become a dominant form of communication for individuals and for businesses looking to market and sell their products and services.
Understanding and responding to customer feedback on Twitter, Facebook and other social media platforms is critical to success, but it takes a lot of work.
A UCF team has developed a technique that accurately detects sarcasm in social media text. The team’s findings were recently published in the journal Entropy.
The team effectively taught the computer model to find patterns that often indicate sarcasm, and combined this with teaching the program to correctly choose keywords in sequences that were more likely to indicate sarcasm. They taught the model to do this by feeding it large data sets, and then they checked it for accuracy.
“The presence of sarcasm in the text is the main obstacle to the performance of sentiment analysis,” says assistant professor of engineering Ivan Garibay.
“Sarcasm isn’t always easy to identify in a conversation, so you can imagine it’s quite difficult for a computer program to do it and do it well. The Multi-Headed Self-Attention Module helps identify critical sarcastic keywords from the entry, and recurring units learn the long-range dependencies between those keywords to better rank the entry text, ”he said. he added.
The team, which includes computer science doctoral student Ramya Akula, began working on this issue under a grant from DARPA that supports the organization’s online social behavior computer simulation program.
Sarcasm has been a major barrier to increasing the accuracy of sentiment analysis, especially on social media, as sarcasm relies heavily on vocal tones, facial expressions, and gestures that cannot be represented in the text, “says Brian Kettler, program director at DARPA. `s Information Innovation Office (I2O).
“Recognizing the sarcasm in online text communication is no easy task because none of these clues are readily available.”
This is one of the challenges that Garibay’s Complex Adaptive Systems Lab (CASL) is studying. CASL is an interdisciplinary research group dedicated to the study of complex phenomena such as the global economy, the global information environment, innovation ecosystems, sustainability, and social and cultural dynamics and evolution.
CASL scientists study these problems using data science, network science, complexity science, cognitive science, machine learning, deep learning, science social, team cognition, among other approaches.
“In a face-to-face conversation, sarcasm can be identified effortlessly using the speaker’s facial expressions, gestures and tone,” Akula explains.
“Detecting sarcasm in text communication is not a trivial task as none of these clues are readily available. Especially with the explosion in internet use, the detection of sarcasm in online communications from social media platforms is much more difficult. “
(With contributions from agencies)