Subjectivity Detection using Genetic Algorithm

An opinion classification system on the notion of opinion subjectivity has been reported. The subjectivity classification system uses Genetic-Based Machine Learning (GBML) technique that considers subjectivity as a semantic problem using syntactic simple string co-occurrence rules that involves grammatical construction and linguistic features. Application of machine learning algorithms in NLP generally experiments with combination of various syntactic and semantic linguistic features to identify the most effective feature set. This is viewed as a multi-objective or multi-criteria optimization search problem. The experiments in the present task start with a large set of possible extractable syntactic, semantic and discourse level feature set. The fitness function calculates the accuracy of the subjectivity classifier based on the feature set identified by natural selection through the process of crossover and mutation after each generation. The proposed technique is tested for English and Bengali and for the news, movie review and blog domains. The system evaluation results show precision of 90.22%, and 93.00% respectively for English NEWS and Movie Review corpus and 87.65% and 90.6% for Bengali NEWS and Blog corpus.