Sarcasm is a core component of the manner in which human beings end up communicating with each other at any given point in time. It can be fairly easy to tell when someone or the other is being sarcastic with you in a real life setting, but in spite of the fact that this is the case, it can be a lot harder to spot in digital contexts and through text. This results in algorithms having a very hard time figuring out if a particular block of text is being sarcastic or not, but a recent sarcasm detection model might change that.
With all of that having been said and now out of the way, it is important to note that this model was developed by researchers at the Symbiosis International University based in the city of Pune, India. The model starts off by processing the text and removing so called “noise words” like “the” and “it”. Following this, it divides the text into bite sized fragments.
One major challenge would be optimizing a model that has to deal with such a multitude of features, and the researchers handled this by using a feature selection process. This gives priority to those features that are deemed as necessary as possible with all things having been considered and taken into account. Sarcasm has certain features that stand out, such as symmetrical uncertainty, so the model is trained to look for those features and ignore any others.
The researchers utilized a collection of algorithms such as Random Forests, Neural Networks, Support Vector Machines as well as a Deep Convolutional Neural Network. These algorithms created the ensemble classifier that helps detect sarcasm.
This model could be a game changer because of the fact that this is the sort of thing that could potentially end up improving natural language processing as well as sentiment analysis, and it could also boost the efficacy of automated customer service programs. Social media monitoring tools could also become a great deal more effective if the model works the way the researchers intend in the near to distant future.
Photo: Digital Information World - AIgen
Read next: These White Collar Jobs Will Be the Most Affected by AI
With all of that having been said and now out of the way, it is important to note that this model was developed by researchers at the Symbiosis International University based in the city of Pune, India. The model starts off by processing the text and removing so called “noise words” like “the” and “it”. Following this, it divides the text into bite sized fragments.
One major challenge would be optimizing a model that has to deal with such a multitude of features, and the researchers handled this by using a feature selection process. This gives priority to those features that are deemed as necessary as possible with all things having been considered and taken into account. Sarcasm has certain features that stand out, such as symmetrical uncertainty, so the model is trained to look for those features and ignore any others.
The researchers utilized a collection of algorithms such as Random Forests, Neural Networks, Support Vector Machines as well as a Deep Convolutional Neural Network. These algorithms created the ensemble classifier that helps detect sarcasm.
This model could be a game changer because of the fact that this is the sort of thing that could potentially end up improving natural language processing as well as sentiment analysis, and it could also boost the efficacy of automated customer service programs. Social media monitoring tools could also become a great deal more effective if the model works the way the researchers intend in the near to distant future.
Read next: These White Collar Jobs Will Be the Most Affected by AI
