What Exactly Do You Mean When You Say “Best”?
June 21, 2017
Technion student develops system that interprets sarcasm on Twitter, and translates it into sarcasm-free language.
Researchers in the Technion 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.
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