Sentiment Classification Using Supervised Sub-Spacing

An important application domain for Machine learning is sentiment classification. Here, the traditional approach is to represent documents using a Bag-Of-Words (BOW) model, where individual terms are used as features. However, the BOW model is unable to sufficiently model the variation inherent in natural language text. Term-relatedness metrics are commonly used to overcome this limitation by capturing latent semantic concepts or topics in documents. However, representations produced using standard term relatedness approaches do not take into account class membership of documents. In this work, we present a novel approach called Supervised Sub-Spacing (S3) for introducing supervision to term-relatedness extraction. S3 works by creating a separate sub-space for each class within which term relations are extracted such that documents belonging to the same class are made more similar to one another. Recent approaches in sentiment classification have proposed combining machine learning with background knowledge from sentiment lexicons for improved performance. Thus, we present a simple, yet effective approach for augmenting S3 with background knowledge from SentiWordNet. Evaluation shows S3 to significantly out perform the state-of-the-art SVM classifier. Results also show that using background knowledge from SentiWordNet significantly improves the performance of S3.