A Hybrid System for Subjectivity Analysis

Author

Rustamov, Samir

Source

Advances in Fuzzy Systems

Issue

Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2018-06-03

Country of Publication

Egypt

No. of Pages

9

Main Subjects

Mathematics

Abstract EN

We suggested different structured hybrid systems for the sentence-level subjectivity analysis based on three supervised machine learning algorithms, namely, Hidden Markov Model, Fuzzy Control System, and Adaptive Neuro-Fuzzy Inference System.

The suggested feature extraction algorithm in our experiment computes a feature vector using statistical textual terms frequencies in a training dataset not having the use of any lexical knowledge except tokenization.

Taking into consideration this fact, the above-mentioned methods may be employed in other languages as these methods do not utilize the morphological, syntactical, and lexical analysis in the classification problems.

American Psychological Association (APA)

Rustamov, Samir. 2018. A Hybrid System for Subjectivity Analysis. Advances in Fuzzy Systems،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-986252

Modern Language Association (MLA)

Rustamov, Samir. A Hybrid System for Subjectivity Analysis. Advances in Fuzzy Systems No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-986252

American Medical Association (AMA)

Rustamov, Samir. A Hybrid System for Subjectivity Analysis. Advances in Fuzzy Systems. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-986252

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-986252