In this article, I will discuss the process of transforming the “cleaned” text data into a sparse matrix. Specifically, I will discuss the use of different vectorizers with simple examples.
The process includes tokenization, removing stopwords, and lemmatization. In this article, I will discuss the process of transforming the “cleaned” text data into a sparse matrix. Specifically, I will discuss the use of different vectorizers with simple examples.
Before we get more technical, I want to introduce two terminologies that are widely used in text analysis. For a collection of text data we want to analyze, we call it corpus. A corpus contains several observations, like news articles, customer reviews, etc. Each of these observations is called a document. I will use these two terms from now on.
The transformation step works as building a bridge that connects the information carried in the text data and the machine learning models. For sentiment analysis, to make sentiment predictions on each document, the machine learning model needs to learn the sentiment score of each unique word in the document, and how many times each word appears there. For example, if we want to conduct sentiment analysis for customer reviews of a product, after training the model, the machine learning models are more than likely to pick up the words like “bad”, “unsatisfied” from negative reviews, while getting words like “awesome”, “great” from positive reviews.
Facing a supervised machine learning problem, to train the model, we need to specify features and target values. Sentiment analysis is solving a classification problem, and in most cases, it is a binary classification problem, with target values defined as positive and negative. The features used to the model are the transformed text data from a vectorizer. The features are constructed differently with different vectorizer. In Scikit Learn, there are three vectorizers, CountVectorizer, TFIDFVectorizer, and HashingVectorizer. Let’s discuss the CountVectorizer first.
Sentimental Analysis Using SVM(Support Vector Machine). Sentimental analysis is the process of classifying various posts and comments of any social media into negative or positive.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
You will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use, and why you should be performing EDA on your next problem.
In this tutorial, I will explain how to calculate the sentiment of a book through a Supervised Learning technique, based on Support Vector Machines (SVM). This tutorial calculates the sentiment analysis of the Saint Augustine Confessions, which can be downloaded from the Gutenberg Project Page. The masterpiece is split in 13 books (chapters).