An Automated Real-Time System for Opinion Mining using a Hybrid Approach

In this paper, a novel idea is being presented to perform Opinion Min ing in a very simp le and efficient manner with the help of the One-Level-Tree (OLT) based approach. To recognize opinions specific for features in customer reviews having a variety of features commingled with diverse emotions. Unlike some previous ventures entirely using one-time structured or filtered data but this is solely based on unstructured data obtained in real-t ime fro m Twitter. The hybrid approach utilizes the associations defined in Dependency Parsing Grammar and fu lly emp loys Double Propagation to extract new features and related new opinions within the review. The Dictionary based approach is used to expand the Opinion Lexicon. W ithin the dependency parsing relations a new relat ion is being proposed to more effectively catch the associations between opinions and features. The three new methods are being proposed, termed as Double Pos itive Double Negative (DPDN), Catch-Phrase Method (CPM) & Negation Check (NC), for perfo rming criteria specific evaluations. The OLT approach conveniently displays the relationship between the features and their opinions in an elementary fashion in the form of a graph. The proposed system achieves splendid accuracy across all do mains and also performs better than the state-of-the-art systems.