Sentiment Analysis Is a Big Suitcase

Erik Cambria, Soujanya Poria, Alexander Gelbukh, Mike Thelwall

Research output: Contribution to journalArticlepeer-review

313 Scopus citations

Abstract

Although most works approach it as a simple categorization problem, sentiment analysis is actually a suitcase research problem that requires tackling many natural language processing (NLP) tasks. The expression 'sentiment analysis' itself is a big suitcase (like many others related to affective computing, such as emotion recognition or opinion mining) that all of us use to encapsulate our jumbled idea about how our minds convey emotions and opinions through natural language. The authors address the composite nature of the problem via a three-layer structure inspired by the 'jumping NLP curves' paradigm. In particular, they argue that there are (at least) 15 NLP problems that need to be solved to achieve human-like performance in sentiment analysis.

Original languageEnglish
Article number8267597
Pages (from-to)74-80
Number of pages7
JournalIEEE Intelligent Systems
Volume32
Issue number6
DOIs
StatePublished - 1 Nov 2017

Keywords

  • artificial intelligence
  • computational linguistics
  • intelligent systems
  • knowledge representation and reasoning
  • machine learning
  • natural language processing
  • sentiment analysis

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