Researchers Develop Sarcasm Detector

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Computer science researchers have developed a sarcasm detector.

Social media has become a dominant form of communication for individuals, and for companies looking to market and sell their products and services. Properly understanding and responding to customer feedback on Twitter, Facebook and other social media platforms is critical for success, but it is incredibly labour intensive.

That is where sentiment analysis comes in. The term refers to the automated process of identifying the emotion – either positive, negative or neutral – associated with text. While artificial intelligence refers to logical data analysis and response, sentiment analysis is akin to correctly identifying emotional communication. A University of Central Florida team developed a technique that accurately detects sarcasm in social media text.

Effectively the team taught the computer model to find 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,” said 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,” he added.

The team, which includes computer science doctoral student Ramya Akula, began working on this problem under a US government DARPA grant that supports the organization’s Computational Simulation of Online Social Behaviour program.

“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 text Recognizing sarcasm in textual online communication is no easy task as none of these cues are readily available,” said Brian Kettler, a program manager in DARPA’s Information Innovation Office.

“In face-to-face conversation, sarcasm can be identified effortlessly using facial expressions, gestures, and tone of the speaker. Detecting sarcasm in textual communication is not a trivial task as none of these cues are readily available. Specially with the explosion of internet usage, sarcasm detection in online communications from social networking platforms is much more challenging,” added Akula.


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Deirdre Tynan

Deirdre Tynan is an award-winning journalist who enjoys bringing the best in news reporting to Spain’s largest English-language newspaper, Euro Weekly News. She has previously worked at The Mirror, Ireland on Sunday and for news agencies, media outlets and international organisations in America, Europe and Asia. A huge fan of British politics and newspapers, Deirdre is equally fascinated by the political scene in Madrid and Sevilla. She moved to Spain in 2018 and is based in Jaen.

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