A Hybrid System for Subjectivity Analysis

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.
Through 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.