Abstract
Machine learning techniques have been used to address various problems and classification of documents is one of the main applications of such techniques. Opinion mining has emerged as an active research domain due to its wide range of applications such as multi-document summarization, opinion mining of documents and users' reviews analysis improving answers of opinion questions in forums. Existing works classify the documents using lexicon-based features only. In this work, four state of the art machine learning techniques have been applied to classify the content into subjective and objective. The subjective content contains opinionative information while objective content contains factual information. The main contribution lies in the introduction of non-lexical features and content based features in addition to the use of a conventional lexicon based feature set. We compare results of four machine learning techniques and discuss performance in diverse categories of lexical and non-lexical features. The comparative analysis has been accomplished using standard performance evaluation measures and experiments have been performed on a real-world dataset of the online forum related to diverse topics. It has been proven that proposed content and non-lexical thread specific features play their role in the classification of subjective and non-subjective content.