A Hybrid System for Subjectivity Analysis
Author
Source
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
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