Model for improving relevant Feature Extraction for Opinion Summarization

The growth of E commerce has led to the abundance growth of opinions on the web, thereby necessitating the task of Opinion Summarization, which in turn has great commercial significance. Feature extraction in Opinion Summarization is very crucial as selection of relevant features reduce the feature space which successfully reduces the complexity of the classification task. The paper suggests extensive pre-processing technique & an algorithm for extracting features from Reviews/Blogs. The proposed technique of Feature Extraction is unsupervised, automated and also domain independent. The improved effectiveness of the proposed approach is demonstrated on a real life dataset that is crawled from many reviewing websites such as CNET, Amazon etc.