Loy Sentiment Analysis

**Working steps**<br> • Create a new experiment in Azure ML Studio<br> • Import Data<br> • Run and Visualize<br> • Add Feature Hashing<br> • Run and Visualize<br> • Drop Col1<br> • Add Split Data<br> • Add Tune Model Hyperparameters<br> • Add Two-class Logistic Regression <br> • Add Score Model<br> • Add Evaluate Model<br> <br> **Dataset**<br> The datasets for this tutorial are from the 'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015, and hosted at the UCI Machine Learning Repository - Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning<br> <br><br> Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.<br><br> **Problem**<br> This problem is centered around predicting if a customer's review has positive or negative sentiment. We will use small wikipedia-detox-datasets (one dataset for training and a second dataset for model's accuracy evaluation) that were processed by humans and each comment has been assigned a sentiment label: <br><br> 0 - nice/positive<br> 1 - toxic/negative<br><br> Using those datasets we will build a model that when predicting it will analyze a string and predict a sentiment value of 0 or 1. <br><br> **ML task - Binary classification**<br> <br> The generalized problem of binary classification is to classify items into one of two classes (classifying items into more than two classes is called multiclass classification). <br><br> predict if an insurance claim is valid or not.<br> predict if a plane will be delayed or will arrive on time.<br> predict if a face ID (photo) belongs to the owner of a device.<br> The common feature for all those examples is that the parameter we want to predict can take only one of two values. In other words, this value is represented by boolean type. <br><br> **Solution** <br> To solve this problem, first we will build an ML model. Then we will train the model on existing data, evaluate how good it is, and lastly we'll consume the model to predict a sentiment for new reviews. <br><br> ![Model][1] [1]: https://raw.githubusercontent.com/laploy/ML.NET/master/Sentiment/Azure-ML-Model.JPG