Social Media has exploded in growth very quickly in recent years and being labeled as a powerful form for individuals to communicate, and organizations to reach further to consumers to market and sell their products or services. With this immense communication tool, it is important for each of us to understand how to properly respond to the feedback from customers on social media platforms for there to be a success, however, this involves greatly the demand for labor. For this success to be achieved, a natural language processing technique called Sentimental Analysis takes center stage. What this means is that a piece of text is computationally identified and classified to determine a writer's overall attitude toward a product, service, etc. is either positive, negative, or neutral in some cases. We know that artificial intelligence correlates to logical data analysis and their response, while sentiment analysis solely focuses on emotional communication identifying. At the University of Central Florida (UCF) a team of experts created a procedure that identifies all forms of sarcasm in a social media text. Initially, the team taught the computer model to observe patterns that involved the usage of sarcasm while also instructing the program to identify clue words that would be most likely be in relation to sarcasm. According to Assistant Professor of Engineering, Ivan Garibay, he stated, “The presence of sarcasm in the text is the main hindrance in the performance of sentiment analysis.” Garibay further states, “Sarcasm is always easy to identify in conversation, so you can imagine it’s pretty challenging for a computer program to do it and do it well.” The UCF team created an intelligible deep learning model that uses various head self-attention and gated recurrent units. What this means is that the various head self-attention module helps in pinpointing essential clue words that relate to sarcasm from input, and the recurrent units master far-reaching responsibilities between these words to better analyze the text.
Source: https://www.sciencedaily.com/releases/2021/05/210507112040.html
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