Our friends at DARPA (Defense Advanced Research Projects Agency) totally get that you're not being serious. At least when they're using their new sarcasm detector.
"With the high velocity and volume of social media data, companies rely on tools to analyze data and to provide customer service. These tools perform tasks such as content management, sentiment analysis, and extraction of relevant messages for the company’s customer service representatives to respond to," UCF Associate Professor of Industrial Engineering and Management Systems, Dr. Ivan Garibay, told Engadget via email. "However, these tools lack the sophistication to decipher more nuanced forms of language such as sarcasm or humor, in which the meaning of a message is not always obvious and explicit. This imposes an extra burden on the social media team, which is already inundated with customer messages to identify these messages and respond appropriately."
As they explain in a study published in the journal, Entropy, Garibay and UCF PhD student Ramya Akula have built “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.”