Communications of the ACM
Natural Language Processing and Affective Computing
A team of researchers has taken advantage of recent advances in signal processing, syntactic analysis, semantic analysis, and pragmatics to use natural language processing to gain deeper understanding of public perception of products, services, brands, an
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Natural-language processing (NLP) uses machine learning and artificial intelligence to comprehend unstructured text and context, enabling the parsing of information as it is received, writes Harvard University professor Mark Esposito and colleagues.
They note NLP functionality can be deconstructed into signal processing, syntactic analysis, semantic analysis, and pragmatics, and recent innovations in the last three areas have advanced the use of NLP for sentiment analysis, which "plays a very important role in decision-making and the ability of a machine to convert human language into machine readable code and convert it into actionable insights."
Computerizing the interpretation of non-vocal cues and other human affects is the purpose of affective computing, which Esposito and colleagues describe as "the next step in the analysis of sentiments and emotions" into quantifiable and actionable results.
"Automating this task to a computer is a true challenge for scientists trying to bridge the barrier between machine-human interfaces," they say.
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