Description

Online reviews are an invaluable resource for web users trying to make decisions regarding products or services. However, the abundance of review content, as well as the unstructured, lengthy, and verbose nature of reviews make it hard for users to locate the appropriate reviews, and distill the useful information. With the recent growth of social networking and micro-blogging services, we observe the emergence of a new type of online review content, consisting of bite-sized, 140 character-long reviews often posted reactively on the spot via mobile devices. These micro-reviews are short, concise, and focused, nicely complementing the lengthy, elaborate, and verbose nature of full-text reviews Location based social networks, such as Foursquare, allow users to post micro-reviews, or tips, about the visited places and to like” previously posted reviews. Tips may help attracting future visitors, besides providing valuable feedback to business owners. Previous efforts to automatically assess the helpfulness of online reviews targeted mainly more verbose and formally structured reviews often exploiting textual features. However, tips are typically much shorter and contain more informal content. Existing system provides review selection problem modeled as a coverage problem. Existing approaches vary in the kind of summary they produce. In this project the system propose a novel mining problem. The system introduce a novel formulation of review selection. This system also propose an Integer Linear Programming (ILP) formulation, and it provide an optimal algorithm. The system also propose a greedy algorithm to identity the optimal solution in coverage and efficiency.