This AI system will help in accurate Sentiment Analysis through sarcasm detector

Sarcasm means the usage of remarks or words that clearly convey the opposite meaning of said statement.Sarcastic comments are usually made in order to criticize someone in a humorous way.Sarcasm is a major impediment for Sentiment Analysis.

As the said statement conveys a different meaning it is difficult  to identify sarcastic statements from a normal one.

But this new AI model can be the solution.Computer Science researchers from University of Central Florida, have developed an AI system that can detect sarcasm in social media posts.

 

Sentiment Analysis

 

• Sentiment Analysis is the process of determining the emotional tone behind words to gain an insight of attitudes,opinions, and emotions expressed  by users  within an online medium.

 

• ML models are employed by companies for sentiment analysis. Logistic Regression and Support Vector Machines(SVM)are used for large scale sentiment analysis.

 

Sarcasm Detection

 

• Sarcasm Detection is a branch of NLP that uses AI methods to learn when the text is sarcastic in tone.The most effective way to detect sarcasm in a statement is through comparison with known truths.

 

• Sarcasm detection is an arduous task .The NLP model finds it tough to distinguish between a positive,negative and a sarcastic statement.The better solution is to take samples of data to determine the percentage cases that present sarcasm.

 

• Through study and proper analysis of sarcastic statements the AI model becomes capable of sarcasm detection.

 

The AI model was taught to trace patterns that indicate sarcasm. Along with that in the teaching programme AI model was asked to pick out cue words in sequences that indicate sarcasm.The model was fed with large datasets further its accuracy was assessed  in a demo sarcasm identification test.

 

The interpretable deep learning model uses multi head self attention and gated recurrent units to identify sarcasm.

 

The presence of sarcasm in the text is the main hindrance in the performance sentiment analysis,” says Ivan Garibay, Assistant Professor of engineering from Complex Adaptive Systems Lab (CASL) at the University of Central Florida.

 

According to Zee News,Computer Science doctoral Ramya Akula said,”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. Especially with the explosion of internet usage, sarcasm detection in online communications from social networking platforms is much more challenging.”