An Automated Real-Time System for Opinion Mining using a Hybrid Approach
Автор: Indrajit Mukherjee, Jasni M Zain, P. K. Mahanti
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 7 vol.8, 2016 года.
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In this paper, a novel idea is being presented to perform Opinion Mining in a very simple 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-time from Twitter. The hybrid approach utilizes the associations defined in Dependency Parsing Grammar and fully employs Double Propagation to extract new features and related new opinions within the review. The Dictionary based approach is used to expand the Opinion Lexicon. Within the dependency parsing relations a new relation is being proposed to more effectively catch the associations between opinions and features. The three new methods are being proposed, termed as Double Positive Double Negative (DPDN), Catch-Phrase Method (CPM) & Negation Check (NC), for performing 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 domains and also performs better than the state-of-the-art systems.
Opinion Mining, Sentiment Analysis, Feature Extraction, Twitter Data Analysis, Graph based Sentiment Analysis, Data Extraction
Короткий адрес: https://sciup.org/15010839
IDR: 15010839
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