An application of hidden Markov models in subjectivity analysis

Author

Rustamov, Samir ; Mustafayev, Elshan ; Clements, Mark A.

Author_Institution

Inst. of Cybern., Baku, Azerbaijan

fYear

2013

fDate

23-25 Oct. 2013

Firstpage

1

Lastpage

4

Abstract

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.

Keywords

data analysis; hidden Markov models; learning (artificial intelligence); pattern classification; text analysis; classification process; feature extraction algorithm; feature vector; grammatical analysis; hidden Markov models; lexical analysis; linguistic knowledge; natural language processing; sentence-level subjectivity detection; speech processing; statistical occurrences; statistical tool; subjectivity analysis; syntactical analysis; tokenization; Classification algorithms; Computational linguistics; Computational modeling; Feature extraction; Hidden Markov models; Natural language processing; Training; Hidden Markov models; machine learning; opinion mining; sentiment analysis; subjectivity detection; text mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Application of Information and Communication Technologies (AICT), 2013 7th International Conference on

Conference_Location

Baku

Print_ISBN

978-1-4673-6419-5

Type

conf

DOI

10.1109/ICAICT.2013.6722756

Filename

6722756