An application of hidden Markov models in subjectivity analysis
Samir Rustamov, Elshan Mustafayev, Mark A. Clements · 2013
Hidden Markov models are a powerful statistical tool and have been used in many areas of speech and natural language processing. In this work, we attempt to detect sentence-level subjectivity by means of hidden Markov model which hasn't been thoroughly investigated for subjectivity analysis. Our feature extraction algorithm calculates a feature vector based on the statistical occurrences of words in a corpus without any linguistic knowledge except tokenization. For this reason, this model can be applied to any language; i.e., there is no lexical, grammatical, syntactical analysis used in the classification process.