Efficient Multi Review Classification using feature extraction technique in the Micro Reviews
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 2
Abstract
Although the content of micro-blogging sites has been studied extensively, micro-reviews are a source of content that has been largely overlooked in the literature. In this paper we study micro-reviews, and we show how they can be used for the problem of review selection. To the best of our knowledge we are the first to mine micro reviews such as foursquare tips and combine them with full-text reviews such as Yelp reviews. Our work introduces a novel formulation of review selection, where the goal is to maximize coverage while ensuring efficiency, leading to novel coverage problems. The coverage problems we consider are of broader interest, and they could find applications to different domains. We consider approximation and heuristic algorithms, and study them experimentally, demonstrating quantitatively and qualitatively the benefits of our approach. We also propose an Integer Linear Programming (ILP) formulation, and provide an optimal algorithm. This allows us to quantify the approximation quality of the greedy heuristics. Experimental results explains the performance of the system against the State of art approach in terms of precision and Recall.
Authors and Affiliations
Miss. A. Jenifaasheer, Dr. Anna Saro Vijendran
Keywords
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