TY - JOUR
T1 - Textual affect sensing and affective communication
AU - Ishizuka, Mitsuru
AU - Neviarouskaya, Alena
AU - Shaikh, Mostafa Al Masum
PY - 2012/10
Y1 - 2012/10
N2 - Unlike sentiment analysis which detects positive, negative, or neutral sentences, textual affect sensing tries to detect more detailed affective or emotional states appearing in text, such as joy, sadness, anger, fear, disgust, surprise and much more. The authors describe here their following two approaches for textual affect sensing: The first one detects nine emotions using a set of rules implemented on the basis of a linguistic compositionality principle for textual affect interpretation. This process includes symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. The second one challenged to recognize 22 emotion types defined in the OCC (Ortony, Clore & Collins) emotion model, which is the most comprehensive emotion model and employs several cognitive variables. In this research, we have shown how these cognitive variables of the emotion model can be computed from linguistic components in text. These two approaches have exploited detailed level analyses of text in two different ways more than ever towards textual affect sensing. Applications towards affective communication are also outlined, including affective instant messaging, affective chat in 3D virtual world, affective haptic interaction, and online news classification relying on affect.
AB - Unlike sentiment analysis which detects positive, negative, or neutral sentences, textual affect sensing tries to detect more detailed affective or emotional states appearing in text, such as joy, sadness, anger, fear, disgust, surprise and much more. The authors describe here their following two approaches for textual affect sensing: The first one detects nine emotions using a set of rules implemented on the basis of a linguistic compositionality principle for textual affect interpretation. This process includes symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. The second one challenged to recognize 22 emotion types defined in the OCC (Ortony, Clore & Collins) emotion model, which is the most comprehensive emotion model and employs several cognitive variables. In this research, we have shown how these cognitive variables of the emotion model can be computed from linguistic components in text. These two approaches have exploited detailed level analyses of text in two different ways more than ever towards textual affect sensing. Applications towards affective communication are also outlined, including affective instant messaging, affective chat in 3D virtual world, affective haptic interaction, and online news classification relying on affect.
KW - Affective communication
KW - Emotions
KW - Instant messaging
KW - Linguistic compositionality
KW - Textual affect sensing
UR - http://www.scopus.com/inward/record.url?scp=84877914096&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877914096&partnerID=8YFLogxK
U2 - 10.4018/jcini.2012100104
DO - 10.4018/jcini.2012100104
M3 - Article
AN - SCOPUS:84877914096
VL - 6
SP - 81
EP - 102
JO - International Journal of Cognitive Informatics and Natural Intelligence
JF - International Journal of Cognitive Informatics and Natural Intelligence
SN - 1557-3958
IS - 4
ER -