Fuzzy Clustering for Semi-supervised Learning - Case Study: Construction of an Emotion Lexicon

We consider the task of semi-supervised classification: extending category labels from a small dataset of labeled examples to a much larger set. We show that, at least on our case study task, unsupervised fuzzy clustering of the unlabeled examples helps in obtaining the hard clusters. Namely, we used the membership values obtained with fuzzy clustering as additional features for hard clustering. We also used these membership values to reduce the confusion set for the hard clustering. As a case study, we use applied the proposed method to the task of constructing a large emotion lexicon by extending the emotion labels from the WordNet Affect lexicon using various features of words. Some of the features were extracted from the emotional statements of the freely available ISEAR dataset; other features were WordNet distance and the similarity measured via the polarity scores in the SenticNet resource. The proposed method classified words by emotion labels with high accuracy.