A Hybrid System for Subjectivity Analysis

Abstract

Identification of subjective data from web documents having opinions within are gaining incrementing interest. Opinions are often views formed by individuals about their sentiments, appraisals, or feelings, etc., not necessarily based on fact or knowledge. Identification of subjectivity attempts to recognize if this written piece of work conveys opinions (personal) or a body of objective facts. This 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 sifting stage in a lot of natural language processing assignments. Trough our experimentation we are aiming to work out techniques in order to establish classifiers able to identify subjective expressions from objective ones. By means of language independent feature weighting, in the experiment the sentence-level subjectivity classification is attained. A subjectivity database from the opinions about films of “Rotten Tomatoes” was deployed as an experiment. In the paper, we suggested different structures of hybrid systems based on various supervised machine learning algorithms such as Hidden Markov Model (HMM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Control System (FCS) which achieved sufficient results. These machine learning methods have been employed for subjectivity analysis individually and our aim is to improve performance of classification by using hybrid systems, which is successfully applied by us in sentiment analysis and natural language call routing problem. 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 classification process.