title: Sentiment analysis using common-sense and context information.
abstract: Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.
authors: Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur 302017, India ; Department of Computer and Engineering, Malaviya National Institute of Technology (MNIT), Malviya Nagar, Jaipur 302017, India.
authors: Basant
authors: 0000-0002-5641-8313
authors: B
authors: Agarwal
authors: Department of Computer and Engineering, Malaviya National Institute of Technology (MNIT), Malviya Nagar, Jaipur 302017, India.
authors: Namita
authors: N
authors: Mittal
authors: Department of Computer and Engineering, Malaviya National Institute of Technology (MNIT), Malviya Nagar, Jaipur 302017, India.
authors: Pooja
authors: P
authors: Bansal
authors: Department of Computer and Engineering, Malaviya National Institute of Technology (MNIT), Malviya Nagar, Jaipur 302017, India.
authors: Sonal
authors: S
authors: Garg
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D012660:Semantics; D012677:Sensation / Q000502:physiology