Sarcasm is one of the oldest and by far the wittiest of tools used by linguists all around the world. A sarcastic comment appears to be one thing and is actually something else entirely. This ambiguity makes the task of sarcasm detection by machines nigh impossible. Nonetheless, our data science team decided to take a stab at it and as a result, we are launching the industry's first solution for detecting sarcasm in text.
Sarcasm is very human and some of the most famous linguists have used it often to comical effects, just read the example below.
"I like long walks, especially when they are taken by people who annoy me."
-Fred Allen (American Comedian)
This post is going to discuss the different aspects of our Sarcasm detection API. The challenges we faced and overcame. Some very funny and tricky examples and some practical use cases. Excited? So are we. Let us dive right in.
Challenges with machine based Sarcasm Detection
Sarcasm is defined as a statement which contradicts itself. This contradiction is conveyed through the context of the statement. Machine Learning researchers have been trying to conceptualize models and algorithms that are capable of detecting sarcasm with industry-grade accuracy. Our attempt at sarcasm detection was wrought with many challenges but the academic progress in this field and the rigor of our data scientists led to the creation of this tool.
"If it was easy, everyone would be doing it, and you wouldn't have an opportunity"
-Bob Parsons (Founder-GoDaddy)
The problem with sarcastic comments is their dependence on the overall scenario. Sarcastic comments have contrasting sentiments in the same sentence which can confuse the AI algorithm trained on sentiment datasets. This problem can be solved by designing an algorithm capable of identifying the idiosyncrasies of a sarcastic comment.
Consider the sentence- "I love the way you lie straight to my face". This sentence can be broken down into contradicting tones, "I love the way you..." (positive) and, "...lie straight to my face" (negative). You see the AI has to work double time and actually look for subtle contradictions to identify the sarcasm.
Some interesting examples of our Sarcasm Detection API
Practical applications of Sarcasm detection
Detecting sarcasm on social media
Today the world is driven by social media. Social networking websites such as Twitter and Facebook get millions of posts every day. These users often leave sarcastic comments related to your brand or services. Our Sarcasm detection API can identify these sarcastic comments which your social media listening tool will misclassify as either positive or negative, therefore affecting the quality of insights.
Detecting sarcasm in user feedback
User feedback analysis can give your business direction for productive growth. Sometimes users respond sarcastically to display their discontent towards a product or service. With sarcasm detection abilities our AI can even identify and accurately classify such responses.
Analyzing movie or serial transcripts
Movies and serials use irony or sarcasm heavily to generate comical moments. Consider the character of Chandler from the very popular 90's comedy Friends, he was known for his witty sarcasm. The transcripts from such serials or movies can be analyzed for the number of sarcastic moments by employing our API which in turn can be used to predict the popularity of a movie or TV show.
Analyzing live shows
Live shows featuring comedians, anchors and other entertainers often feature sarcastic comments as comic relief. The scripts of such shows can be mined to find which comments generate the maximum audience response.
Analyzing political speeches
Good political orators often use sarcasm as a weapon to verbally attack their opposition. The speech script by such politicians can be analyzed to identify sarcasm.
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