Monash University researchers in India and Australia recently published a paper outlining 10 years of research efforts from groups interested in detecting sarcasm in online sources.
The need to accurately identify sarcasm in online sources applies both to artificial intelligence (AI) to assess archive material or interpret existing datasets, and in the field of sentiment analysis, in which a neural network seeks to interpret data based on publicly posted Web material.
Researchers have struggled to quantify sarcasm because it may not be a discrete property in itself, but rather part of a wider range of data-distorting humor. As a result, the researchers say sarcasm may need to be identified as a subset of that in order to be identified programmatically.
They note most of the research projects that have addressed the problem of sarcasm as a hindrance to machine comprehension have studied the problem as it relates to the English and Chinese languages. However, some work also has been done in identifying sarcasm in Italian-language and Dutch tweets.
The new paper details the ways academia has approached the sarcasm problem over the last decade, and concludes the solution to the problem is a sophisticated matrix that has some ability to understand context.
From The Stack (UK)
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