TY - JOUR
T1 - Affective Words and the Company They Keep
T2 - Studying the accuracy of affective word lists in determining sentence and word valence in a domain-specific corpus
AU - Braun, Nadine
AU - Goudbeek, Martijn
AU - Krahmer, Emiel
N1 - Publisher Copyright:
CCBY
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - affect analysis
KW - affective word lists
KW - bag-of-words methods
KW - emotional corpora
U2 - 10.1109/TAFFC.2020.3005613
DO - 10.1109/TAFFC.2020.3005613
M3 - Article
SN - 1949-3045
VL - 13
SP - 1440
EP - 1451
JO - IEEE transactions on affective computing
JF - IEEE transactions on affective computing
IS - 4
ER -