Computer science researchers at the 麻豆原创 have developed a sarcasm detector.
Social media has become a dominant form of communication for individuals, and for companies looking to market and sell their products and services. Properly understanding and responding to customer feedback on Twitter, Facebook and other social media platforms is critical for success, but it is incredibly labor intensive.
That鈥檚 where sentiment analysis comes in. The term refers to the automated process of identifying the emotion 鈥� either positive, negative or neutral 鈥� associated with text. While artificial intelligence refers to logical data analysis and response, sentiment analysis is akin to correctly identifying emotional communication. A 麻豆原创 team developed a technique that accurately detects sarcasm in social media text.
The team鈥檚 findings were recently published in the .
Effectively the team taught the computer model to find patterns that often indicate sarcasm and combined that with teaching the program to correctly pick out cue words in sequences that were more likely to indicate sarcasm. They taught the model to do this by feeding it large data sets and then checked its accuracy.
鈥淭he presence of sarcasm in text is the main hindrance in the performance of sentiment analysis,鈥� says Associate Professor of engineering Ivan Garibay 鈥�00MS 鈥�04PhD. 鈥淪arcasm isn鈥檛 always easy to identify in conversation, so you can imagine it鈥檚 pretty challenging for a computer program to do it and do it well. We developed 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.鈥�
The team, which includes computer science doctoral student Ramya Akula, began working on this problem under a DARPA grant that supports the organization鈥檚 Computational Simulation of Online Social Behavior program.
鈥淪arcasm has been a major hurdle to increasing the accuracy of sentiment analysis, especially on social media, since sarcasm relies heavily on vocal tones, facial expressions and gestures that cannot be represented in text,鈥� says Brian Kettler, a program manager in DARPA鈥檚 Information Innovation Office (I2O). 鈥淩ecognizing sarcasm in textual online communication is no easy task as none of these cues are readily available.鈥�
This is one of the challenges Garibay鈥檚 is studying. CASL is an interdisciplinary research group dedicated to the study of complex phenomena such as the global economy, the global information environment, innovation ecosystems, sustainability, and social and cultural dynamics and evolution. CASL scientists study these problems using data science, network science, complexity science, cognitive science, machine learning, deep learning, social sciences, team cognition, among other approaches.
鈥淚n face-to-face conversation, sarcasm can be identified effortlessly using facial expressions, gestures, and tone of the speaker,鈥� Akula says. 鈥淒etecting sarcasm in textual communication is not a trivial task as none of these cues are readily available. Specially with the explosion of internet usage, sarcasm detection in online communications from social networking platforms is much more challenging.鈥�
Garibay is an associate professor in . He has several degrees including a Ph.D. in computer science from 麻豆原创. Garibay is the director of 麻豆原创鈥檚 Artificial Intelligence and Big Data Initiative. He is also director of the master鈥檚 program in data analytics. His research areas include complex systems, agent-based models, information and misinformation dynamics on social media, artificial intelligence and machine learning. He has more than 75 peer-reviewed papers and more than $9.5 million in funding from various national agencies.
Akula is a doctoral scholar and graduate research assistant at CASL. She has a master鈥檚 degree in computer science from Technical University of Kaiserslautern in Germany and a bachelor鈥檚 degree in computer science engineering from Jawaharlal Nehru Technological University, India.
