Managing Risk and Enhancing Discoverability of Opinion From Online Reviews Using Classification Algorithm
A. Chandrasekar · 2014
A million number of reviews and opinions about any aspect are being posted in numerous blogs, forums, and online sites. This enormous information on worldwide network platforms make them feasible and can be used as source, in applications based on opinion mining and review analysis. The aim of this paper is to discover opinions from online reviews and managing risk in future. Our proposed methodology comprises of phases such as Data pre-processing, Content discovery, Opinion mining and Risk Analaysis. Initially the unstructured data from the web is extracted and preprocessed from the web document. This phase is used for formatting the data before sentiment analysis and mining. The second phase will be classified into two i.e., Feature extraction and opinion extraction. The features like term frequency, Part of Speech are extracted from the words in the documents. After feature extraction, we extract useful information related to the item's features and use it to rate them as positive, neutral, or negative. This phase will be done by supervised learning algorithm decision tree and ranking based classifier with the help of features extracted. In the final step analysing and manging risk will be done.