Qualitative risk avoidance methodology for categorization of mined opinions from online reviews

The rapid development of the Internet has spectacularly changed the mode that people articulate their opinions. Nowadays, people can liberally send reviews on any aspect of various websites to convey their individual opinions. As the opinions communicate the subjective thoughts, estimation, and conjectures of people in natural language, this type of contents contributed by internet users have been well acknowledged as valuable information. It can be oppressed to evaluate public reviews on a specific topic or product in order to classify out user like or dislike, etc. Opinion blend and mining are the methods to explore and summarize opinions from reviews in order to understand public perception on a specific topic or an entity. The intend of this paper is to determine opinions from online reviews and managing risk in future. Our projected methodology involves phases such as Data preprocessing, Content discovery, review mining and Risk investigation. Initially the formless data from the websites is extracted and preprocessed. This stage is used for formatting the fact before sentiment classification and mining. Finally, managing risk by using a qualitative risk assessment methodology for categorizing opinions from online reviews is done.