Words often convey affect -- emotions, feelings, and attitudes. Lexicons of
word-affect association have applications in automatic emotion analysis and
natural language generation. However, existing lexicons indicate only coarse
categories of affect association. Here, for the first time, we create an affect
intensity lexicon with real-valued scores of association. We use a technique
called best-worst scaling that improves annotation consistency and obtains
reliable fine-grained scores. The lexicon includes terms common from both
general English and terms specific to social media communications. It has close
to 6,000 entries for four basic emotions. We will be adding entries for other
affect dimensions shortly.
%0 Generic
%1 mohammad2017affect
%A Mohammad, Saif M.
%D 2017
%K basicemotions kallimachos sentimentanalysis
%T Word Affect Intensities
%U http://arxiv.org/abs/1704.08798
%X Words often convey affect -- emotions, feelings, and attitudes. Lexicons of
word-affect association have applications in automatic emotion analysis and
natural language generation. However, existing lexicons indicate only coarse
categories of affect association. Here, for the first time, we create an affect
intensity lexicon with real-valued scores of association. We use a technique
called best-worst scaling that improves annotation consistency and obtains
reliable fine-grained scores. The lexicon includes terms common from both
general English and terms specific to social media communications. It has close
to 6,000 entries for four basic emotions. We will be adding entries for other
affect dimensions shortly.
@misc{mohammad2017affect,
abstract = {Words often convey affect -- emotions, feelings, and attitudes. Lexicons of
word-affect association have applications in automatic emotion analysis and
natural language generation. However, existing lexicons indicate only coarse
categories of affect association. Here, for the first time, we create an affect
intensity lexicon with real-valued scores of association. We use a technique
called best-worst scaling that improves annotation consistency and obtains
reliable fine-grained scores. The lexicon includes terms common from both
general English and terms specific to social media communications. It has close
to 6,000 entries for four basic emotions. We will be adding entries for other
affect dimensions shortly.},
added-at = {2017-05-10T07:37:45.000+0200},
author = {Mohammad, Saif M.},
biburl = {https://www.bibsonomy.org/bibtex/209129e13dead6ea48ee673d0834d02d0/albinzehe},
description = {[1704.08798] Word Affect Intensities},
interhash = {cbed8256a8b9186ee526c4e610ce3cf1},
intrahash = {09129e13dead6ea48ee673d0834d02d0},
keywords = {basicemotions kallimachos sentimentanalysis},
note = {cite arxiv:1704.08798},
timestamp = {2017-05-10T07:37:45.000+0200},
title = {Word Affect Intensities},
url = {http://arxiv.org/abs/1704.08798},
year = 2017
}