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The Comprehension of Figurative Language: What Is the Influence of Irony and Sarcasm on NLP Techniques?

  • Leila WeitzelEmail author
  • Ronaldo Cristiano Prati
  • Raul Freire Aguiar
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 639)

Abstract

Due to the growing volume of available textual information, there is a great demand for Natural Language Processing (NLP) techniques that can automatically process and manage texts, supporting the information retrieval and communication in core areas of society (e.g. healthcare, business, and science). NLP techniques have to tackle the often ambiguous and linguistic structures that people use in everyday speech. As such, there are many issues that have to be considered, for instance slang, grammatical errors, regional dialects, figurative language , etc. Figurative Language (FL), such as irony , sarcasm , simile, and metaphor, poses a serious challenge to NLP systems. FL is a frequent phenomenon within human communication, occurring both in spoken and written discourse including books, websites, fora, chats, social network posts, news articles and product reviews. Indeed, knowing what people think can help companies, political parties, and other public entities in strategizing and decision-making polices. When people are engaged in an informal conversation, they almost inevitably use irony (or sarcasm) to express something else or different than stated by the literal sentence meaning. Sentiment analysis methods can be easily misled by the presence of words that have a strong polarity but are used sarcastically, which means that the opposite polarity was intended. Several efforts have been recently devoted to detect and tackle FL phenomena in social media. Many of applications rely on task-specific lexicons (e.g. dictionaries, word classifications) or Machine Learning algorithms. Increasingly, numerous companies have begun to leverage automated methods for inferring consumer sentiment from online reviews and other sources. A system capable of interpreting FL would be extremely beneficial to a wide range of practical NLP applications. In this sense, this chapter aims at evaluating how two specific domains of FL, sarcasm and irony, affect Sentiment Analysis (SA) tools. The study’s ultimate goal is to find out if FL hinders the performance (polarity detection) of SA systems due to the presence of ironic context. Our results indicate that computational intelligence approaches are more suitable in presence of irony and sarcasm in Twitter classification.

Keywords

Sentiment analysis Irony Sarcasm Figurative language Machine learning Natural language processing 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Leila Weitzel
    • 1
    Email author
  • Ronaldo Cristiano Prati
    • 2
  • Raul Freire Aguiar
    • 2
  1. 1.Departamento de Computação Rio das Ostras RjUniverisidade Federal Fluminense (UFF)Rio de JaneiroBrazil
  2. 2.Centro de Matemática, Computação e CogniçãoUniversidade Federal do ABC (UFABC)Santo AndréBrazil

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