Research Article A Hybrid System for Subjectivity Analysis Samir Rustamov 1,2 1 ADA University, Ahmadbey Aghaoglu Street 11, Bakı AZ1008, Baku, Azerbaijan 2 Institute of Control Systems of Azerbaijan National Academy of Sciences, Bakhtiyar Vahabzadeh Street 9, AZ1141, Baku, Azerbaijan Correspondence should be addressed to Samir Rustamov; samir.rustamov@gmail.com Received 30 June 2017; Accepted 3 May 2018; Published 3 June 2018 Academic Editor: Ferdinando Di Martino Copyright © 2018 Samir Rustamov. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We suggested diferent 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. he 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 classiication problems. 1. Introduction Identiication of subjective data from web documents having opinions within are gaining incrementing interest. Opinions are oten views formed by individuals about their sentiments, appraisals, or feelings, etc., not necessarily based on fact or knowledge. Identiication of subjectivity attempts to recog- nize if this written piece of work conveys opinions (personal) or a body of objective facts [1]. his analysis has been utilized in many natural language and text mining solutions. With the aim of generating more instructive data, subjectivity detection has been employed as a primary siting stage in a lot of natural language processing assignments. hrough our experimentation we are aiming to work out techniques in order to establish classiiers able to identify subjective expressions from objective ones. By means of language independent feature weighting, in the experiment the sentence-level subjectivity classiication is attained. A subjectivity database from the opinions about ilms of “Rotten Tomatoes” [2] was deployed as an experiment. In the paper, we suggested diferent structures of hybrid systems based on various supervised machine learning algorithms such as Hidden Markov Model (HMM), Adap- tive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Control System (FCS) which achieved suicient results. hese machine learning methods have been employed for subjectivity analysis individually [3, 4] and our aim is to improve performance of classiication by using hybrid sys- tems, which is successfully applied by us in sentiment analysis and natural language call routing problem [5, 6]. Our feature extraction algorithm computes a feature vector using the statistical textual terms frequencies in the corpus 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 lexical, grammatical, and syntactical analysis within the classiication process. 2. Related Work With the aim of recognizing the subjective data in written piece of work or speech, various nonsimilar supervised and unsupervised learning algorithms have been recently analyzed. A bootstrap technique was employed by Rilof and Wiebe [7] with the aim of studying subjectivity classiiers from a nonannotated texts collection. An identical method was applied by Wiebe and Rilof [8]; however, they studied objective sentences aside from subjective sentences. With the objective to classify subjective and objective sen- tences, a MinCut based algorithm was deployed by Pang and Lee [9]. hrough this experiment they targeted eliminating Hindawi Advances in Fuzzy Systems Volume 2018, Article ID 2371621, 9 pages https://doi.org/10.1155/2018/2371621