How to build a Text Mining Platform скачать в хорошем качестве

How to build a Text Mining Platform

Tiger Zhang & Lutz Finger on Text Mining Today more than ever before, we have access raw data in the form of texts. Businesses around the world store text discussions from their market research, customer care discussions, or brand relevant conversation on social media. While it is clear that texts contain valuable information, it is often less clear on how best texts can be analyzed at scale. In this class, we will share how we at LinkedIn built a scalable text-mining platform to uncover insights from text data. We will focus on two important components: THEME DISCOVERY of new content and how to CLASSIFY existing text. Using both features, we can detect emerging trends within reviews, customer care discussions and market research data. You will learn: THEME DISCOVERY - information extraction Theme recognition is a highly complex task due to the multi-facetted nature of our language. Theme Recognition (without requiring manual reviews) is, however, the main component of any text-mining platform. We will introduce an innovation in information extraction using part of speech tagging (currently patent pending) to uncover themes within textual data. TEXT CLASSIFICATIONS - Supervised Machine Learning Another important component of our NLP platform is the ability to classify text via supervised machine learning algorithms such as support vector machine (SVM). The ability to classify serves many business use-cases ranging from sentiment analytics to product identification. You will learn in our talk how to cater to those different requirements via a flexible platform setup. VALUE of DATA - Member Feedback The combined ability of Themes Discovery (new content and ideas) as well as Classifications (standard measure) creates a very effective framework to get business insights out of text data. We will demonstrate this on the use case of classifying and responding to member feedback.