Feature Based Concept Level Sentiment Analysis for Online Restaurant Reviews

Abstract

Present day, World Wide Web o ers a great means to share knowledge, be- coming one large repository of valuable opinions of di erent people on numerous products and services. It is not straightforward to access these opinions because the available number is vast, and understanding what is actually meant has always been problematic with the complexity of human language. These texts could contain various attributes (or features) forming complex opinion relation- ships hidden inside large numbers of sentences. Most of the existing approaches for opinion mining are based on word- level analysis of texts and are able to detect only explicitly expressed opinions. Concept-level opinion mining attempts to go beyond mere word level analysis, providing a more semantic inspection of text through the use of semantic net- works, empowering novel approaches to sentiment analysis that can be used in di erent domains. Commonsense knowledge, spans a huge portion of human experience, encompassing knowledge about the spatial, physical, social, tempo- ral, and psychological aspects of everyday life. This research proposes a novel approach to concept-level opinion mining by relying on publicly available com- monsense knowledge, with the assumption that closely related concepts in the knowledge-base are likely to share similar sentiments to each other. This study tries to simulate the cognitive ability of human brain, up to some extent by identifying concepts, process those with the aid of large seman- tic knowledge-base that we created and natural language processing techniques. Concept extraction from natural language text achieved in two steps, part-of- speech based extraction and dependency based extraction. The experiment show state of the art promising results, proving we have achieved successfully using of human knowledge with the domain knowledge to analyze sentiments.