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.
Rustamov et al. REF introduced a novel ''Pruned ICF Weighting Coefficient'', which utilizes the principle of fuzzy control and statistical occurrence of words in corpus without any lexical knowledge as emotional features to improve the accuracy for subjectivity detection.
Sentence-Level Subjectivity Detection Using Neuro-Fuzzy Models
WASSA
Computer Science