Improving sentiment classification through distinct word selection

While the performance of sentiment classification has steadily risen through the introduction of various feature-based methods and distributed representation-based approaches, less attention was given to the qualitative aspect of classification, for instance, the identification of useful words in individual opinion texts. We present an approach using set operations for identifying useful words for sentiment classification, and employ truncated singular value decomposition (SVD), a classic low-rank matrix decomposition technique for document retrieval, in order to tackle the issue of both synonymy and noise removal. The sentiment classification performance of our approach, which concatenates three kinds of features, outperforms the existing word-based and distributed word representation-based methods and is comparable to the existing state of the art distributed document representation-based approaches.