Economic terms in the news during the Great Recession  Paid

A diachronic sentiment and collocational analysis

by Javier Fernández-Cruz (Author) , Antonio Moreno-Ortiz (Author)
©2024, Monographs, 192 Pages
Linguistics

Series: Linguistic Insights, Volume 303

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eBook


This book explores the evolution of sentiment in economic terms in the press during financial crises applying a combination of sentiment analysis techniques and usage fluctuation analysis on a diachronic corpus derived from editorials in quality newspapers during the Great Recession. The book uncovers two key findings: first, certain economic terms become event words during times of crisis due to their increased use in the press and the general public, revealing rapid semantic changes in economic terms caused by major socio-economic events. Second, sentiment-laden collocations are found to be influenced by culture, highlighting language’s adaptability to financial upheavals. This work proposes an innovative methodology that combines lexicon-based Sentiment Analysis, Corpus Linguistics, and qualitative Discourse Analysis to shed light on how language shapes economic discourse, making it a valuable resource for scholars exploring the relationship between language and historic events.

  1. Cover
  2. Title
  3. Copyright
  4. About the author
  5. About the book
  6. This eBook can be cited
  7. Table of Contents
  8. 1 Introduction
  9. 2 Research design
    1. 2.1 Research questions and objectives
    2. 2.2 Corpus
    3. 2.3 Method
    4. 2.4 Instruments
      1. 2.4.1 Sentiment analysis software
      2. 2.4.2 Corpus statistics tools
  10. 3 The influence of the economy and the press on language
    1. 3.1 Online content and opinion
  11. 4 Evaluative language
    1. 4.1 Terms and concepts in evaluative language
    2. 4.2 Definition of evaluation
    3. 4.3 Functions of evaluation
    4. 4.4 Markers of evaluation
    5. 4.5 Evaluation in journalistic genres
  12. 5 Formal models for the study of evaluative language
    1. 5.1 Liu’s model
    2. 5.2 Benamara’s model
  13. 6 Sentiment analysis
    1. 6.1 Definition and applications
    2. 6.2 Classification levels
      1. 6.2.1 Document-level sentiment classification
      2. 6.2.2 Sentence-level sentiment classification
      3. 6.2.3 Aspect-level sentiment classification
    3. 6.3 Machine learning approaches to sentiment analysis
    4. 6.4 Lexicon-based sentiment analysis
      1. 6.4.1 Sentiment lexicon generation
      2. 6.4.2 Available sentiment lexicons
      3. 6.4.3 Contextual valence shifters
    5. 6.5 Domain-specific sentiment classification
      1. 6.5.1 Economics and sentiment analysis
      2. 6.5.2 Characteristics of the economic-financial domain in relation to sentiment analysis
      3. 6.5.3 Economic sentiment dictionaries
  14. 7 Language change and semantic prosody
    1. 7.1 Semantic change, sentiment, and event words
    2. 7.2 Semantic prosody
  15. 8 Data analysis
    1. 8.1 Term 1: ‘credit’
      1. 8.1.1 Sentiment analysis
      2. 8.1.2 Usage fluctuation analysis
      3. 8.1.2.1 First phase (2007)
      4. 8.1.2.2 Second phase (2008–2012)
      5. 8.1.2.3 Third phase (2013–2015)
    2. 8.2 Term 2: ‘debt’
      1. 8.2.1 Sentiment analysis
      2. 8.2.2 Usage fluctuation analysis
      3. 8.2.2.1 First phase (2007)
      4. 8.2.2.2 Second phase (2008–2011)
      5. 8.2.2.3 Third phase (2012–2015)
    3. 8.3 Term 3: ‘markets’
      1. 8.3.1 Sentiment analysis
      2. 8.3.2 Usage fluctuation analysis
      3. 8.3.2.1 First phase (2007)
      4. 8.3.2.2 Second phase (2008–2011)
      5. 8.3.2.3 Third phase (2012–2014)
      6. 8.3.2.4 Fourth phase (2015)
    4. 8.4 Term 4: ‘housing’
      1. 8.4.1 Sentiment analysis
      2. 8.4.2 Usage fluctuation analysis
      3. 8.4.2.1 First phase (2007–2012)
      4. 8.4.2.2 Second phase (2013–2015)
  16. 9 Discussion and conclusions
  17. List of Figures
  18. List of Tables
  19. References
Pages:
192
Year:
2024
ISBN (HARDBACK):
9783034347785 (Active)
ISBN (EPUB):
9783034347822 (Active)
ISBN (PDF):
9783034347815 (Active)
Language:
English
Published:
Bern, Berlin, Bruxelles, New York, Oxford, Warszawa, Wien, 2024. 192 pp., 11 fig. b/w, 12 tables.
Javier Fernández-Cruz is Lecturer at the University of Málaga and a member of the Tecnolengua Research Group. He has worked at universities in France, Italy and Ecuador. With a dedicated focus on digital humanities, his research efforts concentrate on corpus linguistics, sentiment analysis and specialized languages.
Antonio Moreno Ortiz is Associate Professor at the University of Malaga. His research focuses on computational and corpus linguistics, including the development of software and resources. He leads the Tecnolengua Research Group, which focuses on computer-mediated communication, with special emphasis on sentiment analysis and social media.

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