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Sentiment analysis is a special case of text mining that is increasingly important in business intelligence and social media analysis. In this lab, we’ll build an experiment for sentiment analysis of documents in SharePoint, using Microsoft Azure Machine Learning Studio. For example, sentiment analysis of document reviews and comments can help organisations monitor appreciation and utilisation of their IP (Intellectual Property), or help users identify opinion polarity before accessing a resource. This experiment demonstrates the use of the Feature Hashing, Execute R Script and Filter-Based Feature Selection modules to train a sentiment analysis engine. Using a data-driven machine learning approach, document access information and comments are used to train a model using the Two-Class Support Vector Machine, and the trained model is used to predict the opinion polarity of documents in SharePoint sites. The output predictions can be aggregated over document tags containing a certain keyword, in order to find out the overall sentiment for each element of the taxonomy, and lastly published as a Web Service in Azure, for access by third-party applications.