Language model-based sentence classification for opinion question answering systems

In this paper, we discuss an essential component for classifying opinionative and factual sentences in an opinion question answering system. We propose a language model-based approach with a Bayes classifier. This classification model is used to filter sentence retrieval outputs in order to answer opinionative questions. We used Subjectivity dataset for our experiments and applied different state-of-the-art smoothing methods. The results show that our proposed technique significantly outperforms current standard classification methods including support vector machines. The accuracy is improved from 90.49% to 93.35%.