TY - JOUR
T1 - Enhanced senticnet with affective labels for concept-based opinion mining
AU - Poria, Soujanya
AU - Gelbukh, Alexander
AU - Hussain, Amir
AU - Howard, Newton
AU - Das, Dipankar
AU - Bandyopadhyay, Sivaji
PY - 2013
Y1 - 2013
N2 - A methodology to automatically assign emotion labels to SenticNet concepts is proposed. SenticNet was supplied with affective WordNet-Affect (WNA) compatible labels using a machine learning algorithm. The WNA list dataset consists of six word lists corresponding to Ekman's six basic emotions, anger, disgust, fear, joy, sadness, and surprise. The dataset contains 606 synsets, of which all but two are assigned exactly one label each. If the synsets are broken down into individual concepts, the dataset contains 1,536 concepts. For each SenticNet concept, ISEAR statistical features of its occurrences and cooccurrences with other SenticNet concepts in ISEAR statements were extracted. English WordNet 3.0 was used to measure the semantic distance between two words. This work opens up multiple directions for future research, such as using other types of monolingual or multilingual corpora as a source of features to improve the accuracy or to label more concepts.
AB - A methodology to automatically assign emotion labels to SenticNet concepts is proposed. SenticNet was supplied with affective WordNet-Affect (WNA) compatible labels using a machine learning algorithm. The WNA list dataset consists of six word lists corresponding to Ekman's six basic emotions, anger, disgust, fear, joy, sadness, and surprise. The dataset contains 606 synsets, of which all but two are assigned exactly one label each. If the synsets are broken down into individual concepts, the dataset contains 1,536 concepts. For each SenticNet concept, ISEAR statistical features of its occurrences and cooccurrences with other SenticNet concepts in ISEAR statements were extracted. English WordNet 3.0 was used to measure the semantic distance between two words. This work opens up multiple directions for future research, such as using other types of monolingual or multilingual corpora as a source of features to improve the accuracy or to label more concepts.
UR - http://www.scopus.com/inward/record.url?scp=84898734877&partnerID=8YFLogxK
U2 - 10.1109/MIS.2013.40
DO - 10.1109/MIS.2013.40
M3 - Artículo
SN - 1541-1672
VL - 28
SP - 2
EP - 9
JO - IEEE Intelligent Systems
JF - IEEE Intelligent Systems
IS - 2
ER -