Affective Words and the Company They Keep: Studying the accuracy of affective word lists in determining sentence and word valence in a domain-specific corpus

Nadine Braun, Martijn Goudbeek, Emiel Krahmer

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In this study, we explore whether and how linguistic and pragmatic context can change individual word valence and emotionality in two parts. In the first part, we investigate whether sentence contexts retrieved from a domain-specific corpus (soccer) bias individual word affect. We then examine whether word valence with and without context accurately indicates sentence valence. In the second part, we compare word ratings from the first part to four different existing affective lexicons, with different levels of sensitivity to semantic and pragmatic context, and examine their accuracy in determining sentence valence. Results show a significant difference between words with and without context, the former more accurate in determining sentence valence than the latter. The preexisting lexicons were found to be similar to the individual word ratings collected in the first part of the study, with human-evaluated, context-sensitive lexicons being the most accurate in determining sentence valence. We discuss implications for emotion theory and bag-of-words approaches to sentiment analysis.

Original languageEnglish
JournalIEEE transactions on affective computing
DOIs
Publication statusPublished - 2020

Keywords

  • affect analysis
  • affective word lists
  • bag-of-words methods
  • emotional corpora

Fingerprint

Dive into the research topics of 'Affective Words and the Company They Keep: Studying the accuracy of affective word lists in determining sentence and word valence in a domain-specific corpus'. Together they form a unique fingerprint.

Cite this