Enhanced senticnet with affective labels for concept-based opinion mining
Poria S., Gelbukh A., Hussain A., Howard N., Das D., Bandyopadhyay S.
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.