System Detects & Translates Sarcasm on Social Media

Technion student develops system that interprets sarcasm on Twitter, and translates it into sarcasm-free language

Article ID: 676871

Released: 21-Jun-2017 4:50 PM EDT

Source Newsroom: American Technion Society

Newswise — Researchers in the Technion-Israel Institute of Technology Faculty of Industrial Engineering and Management have developed a system for interpreting sarcastic statements in social media. The system, developed by graduate student Lotam Peled, under the guidance of Assistant Professor Roi Reichart, is called Sarcasm SIGN (sarcasm Sentimental Interpretation GeNerator).

“There are a lot of systems designed to identify sarcasm, but this is the first that is able to interpret sarcasm in written text,” said Peled. “We hope in the future, it will help people with autism and Asperger’s, who have difficulty interpreting sarcasm, irony and humor.”

Based on machine translation, the new system turns sarcastic sentences into honest (non-sarcastic) ones. It will, for example, turn a sarcastic sentence such as, “The new ‘Fast and Furious’ movie is awesome. #sarcasm” into the honest sentence, “The new Fast and Furious movie is terrible.”

Despite the vast development in this field, and the successes of sentiment analysis applications on “social media intelligence,” existing applications do not know how to interpret sarcasm, where the writer writes the opposite of what (s)he actually means.

In order to teach the system to produce accurate interpretations, the researchers compiled a database of 3,000 sarcastic tweets that were tagged with #sarcasm, where each tweet was interpreted into a non-sarcastic expression by five human experts. In addition, the system was trained to identify words with strong sarcastic sentiments – for example, the word “best” in the tweet, “best day ever” – and to replace them with strong words that reveal the true meaning of the text. The system was examined by a number of (human) judges, who gave its interpretations high scores of fluency and adequacy, agreeing that in most cases it produced a semantically and linguistically correct sentence.

Automatic identification and analysis of sentiment in text is a very complex challenge being explored by many researchers around the world because of its commercial potential and scientific importance. Sentiment identification could be used in social, commercial, and other applications to improve communication between people and computers, and between social media users.

The Technion-Israel Institute of Technology is a major source of the innovation and brainpower that drives the Israeli economy, and a key to Israel’s renown as the world’s “Start-Up Nation.” Its three Nobel Prize winners exemplify academic excellence. Technion people, ideas and inventions make immeasurable contributions to the world including life-saving medicine, sustainable energy, computer science, water conservation and nanotechnology. The Joan and Irwin Jacobs Technion-Cornell Institute is a vital component of Cornell Tech, and a model for graduate applied science education that is expected to transform New York City’s economy.

American Technion Society (ATS) donors provide critical support for the Technion—more than $2 billion since its inception in 1940. Based in New York City, the ATS and its network of supporters across the U.S. provide funds for scholarships, fellowships, faculty recruitment and chairs, research, buildings, laboratories, classrooms and dormitories, and more.


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