TY - GEN
T1 - A hybrid mood classification approach for blog text
AU - Jung, Yuchul
AU - Park, Hogun
AU - Myaeng, Sung Hyon
PY - 2006
Y1 - 2006
N2 - As an effort to detect the mood of a blog, regardless of the length and writing style, we propose a hybrid approach to detecting blog text's mood, which incorporates commonsense knowledge obtained from the general public (ConceptNet) and the Affective Norms English Words (ANEW) list. Our approach picks up blog text's unique features and compute simple statistics such as term frequency, n-gram, and point-wise mutual information (PMI) for the SVM classification method. In addition, to catch mood transitions in a given blog text, we developed a paragraph-level segmentation based on a mood flow analysis using a revised version of the GuessMood operation of ConceptNet and an ANEW-based affective sensing module. For evaluation, a mood corpus comprised of real blog texts has been built semi-automatically. Our experiments using the corpus show meaningful results for 4 mood types: happy, sad, angry, and fear.
AB - As an effort to detect the mood of a blog, regardless of the length and writing style, we propose a hybrid approach to detecting blog text's mood, which incorporates commonsense knowledge obtained from the general public (ConceptNet) and the Affective Norms English Words (ANEW) list. Our approach picks up blog text's unique features and compute simple statistics such as term frequency, n-gram, and point-wise mutual information (PMI) for the SVM classification method. In addition, to catch mood transitions in a given blog text, we developed a paragraph-level segmentation based on a mood flow analysis using a revised version of the GuessMood operation of ConceptNet and an ANEW-based affective sensing module. For evaluation, a mood corpus comprised of real blog texts has been built semi-automatically. Our experiments using the corpus show meaningful results for 4 mood types: happy, sad, angry, and fear.
UR - https://www.scopus.com/pages/publications/33749544189
U2 - 10.1007/11801603_141
DO - 10.1007/11801603_141
M3 - Conference contribution
AN - SCOPUS:33749544189
SN - 3540366679
SN - 9783540366676
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1099
EP - 1103
BT - PRICAI 2006
PB - Springer Verlag
T2 - 9th Pacific Rim International Conference on Artificial Intelligence
Y2 - 7 August 2006 through 11 August 2006
ER -