Sentence-Level Subjectivity Detection Using Neuro-Fuzzy Models

In this work, we attempt to detect sentencelevel subjectivity by means of two supervised machine learning approaches: a Fuzzy Control System and Adaptive Neuro-Fuzzy Inference System. Even though these methods are popular in pattern recognition, they have not been thoroughly investigated for subjectivity analysis. We present a novel “Pruned ICF Weighting Coefficient,” which improves the accuracy for subjectivity detection. Our feature extraction algorithm calculates a feature vector based on the statistical occurrences of words in a corpus without any lexical knowledge. For this reason, these machine learning models can be applied to any language; i.e., there is no lexical, grammatical, syntactical analysis used in the classification process.