Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization

Gamgarn Somprasertsri (King Mongkut's Institute of Technology Ladkrabang, Thailand)

Pattarachai Lalitrojwong (King Mongkut's Institute of Technology Ladkrabang, Thailand)

Abstract: Online customer reviews is considered as a significant informative resource which is useful for both potential customers and product manufacturers. In web pages, the reviews are written in natural language and are unstructured-free-texts scheme. The task of manually scanning through large amounts of review one by one is computational burden and is not practically implemented with respect to businesses and customer perspectives. Therefore it is more efficient to automatically process the various reviews and provide the necessary information in a suitable form. The high-level problem of opinion summarization addresses how to determine the sentiment, attitude or opinion that an author expressed in natural language text with respect to a certain feature. In this paper, we dedicate our work to the main subtask of opinion summarization. The task of product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the performance of opinion orientation identification. It is important to properly identify the semantic relationships between product features and opinions. We proposed an approach for mining product feature and opinion based on the consideration of syntactic information and semantic information. By applying dependency relations and ontological knowledge with probabilistic based model, the result of our experiments shows that our approach is more flexible and effective.

Keywords: customer feedback, dependency grammars, maximum entropy, opinion mining, opinion summarization, text mining

Categories: H.2.8, H.3.1, H.3.5, I.2.7