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In recent years, consumers are performing a pilot investigation using online resources before making any decision of purchase. One of the most popular social blogging online medium is twitter. The opinions collected from twitter at any point of frame in real world scenario are tending towards class imbalance in nature. The existing algorithms for opinion mining can work better on class balance nature, where opinions (positive and negative) are almost balance. In this paper, we propose a novel approach known as Improved Opinion Mining using Under Sampling (IOMUS) to efficiently summarize the reviews of class imbalance opinion mining corpus. The experimental set up is performed on the collection of opinion mining class imbalance dataset consisting of "1155" instances. The experimental results suggest that improved performance is obtained by the proposed IOMUS algorithm than the traditional approach.

There are various kinds of valuable semantic information about real-world entities embedded in web pages and databases. Extracting and integrating these entity information from the Web is of great significance. Comparing to traditional information extraction problems, web entity extraction needs to solve several new challenges to fully take advantage of the unique characteristic of the Web. In this paper, we introduce our recent work on statistical extraction of structured entities, named entities, entity facts and relations from Web. We also briefly introduce iKnoweb, an interactive knowledge mining framework for entity information integration. We will use two novel web applications, Microsoft Academic Search (aka Libra) and EntityCube, as working examples.