Classify payment text easily

Aug 6th, 2022
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How to classify payment text

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hi everyone my name is Michelle Sharfstein and today Im going to talk to you about fast text which is a library for text classification so in this tech talk Im gonna cover a few different things first why is text classification important and what are some applications of text classification then Im going to look at fast text the library itself and talk about how it builds classification models and then how it uses them and evaluates them and then Ill go through an example or two so that you can see it in practice so first why text classification there are actually a lot of applications of text classification that are incredibly relevant for us every single day so the first are around web application search and content ranking so if Im a user on on a website I want to find all of the texts that are related to sports its really important for every piece of text to be able to classified into its correct category so that when Im looking for sports all of the most relevant content c

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Classification of Texts: How Do They Classify? Explained in Detail Descriptive texts. Expository Texts. Argumentative texts. Normative Texts. Journalistic Texts. Informative function. Aesthetic Function.
More formally, text classification is an analytical process that takes any text document as input and assigns a label (or classification) from a predetermined set of class labels.
If you have a group of things, such as fruits or geometric shapes, you can classify them based on the property that they possess. For example, you can classify the apples in one category, the bananas in another, and so on. Similarly, geometric shapes can be classified as triangles, quadrilaterals, and so on.
Text Classification Workflow Step 1: Gather Data. Step 2: Explore Your Data. Step 2.5: Choose a Model* Step 3: Prepare Your Data. Step 4: Build, Train, and Evaluate Your Model. Step 5: Tune Hyperparameters. Step 6: Deploy Your Model.
Linear Support Vector Machine is widely regarded as one of the best text classification algorithms.
Text classification is one of the core challenges in natural language processing with diverse applications such as sentiment analysis, topic labelling, spam detection, and intent identification. You can do text classification in two ways: manual or automatic.
Text Classification Workflow Step 1: Gather Data. Step 2: Explore Your Data. Step 2.5: Choose a Model* Step 3: Prepare Your Data. Step 4: Build, Train, and Evaluate Your Model. Step 5: Tune Hyperparameters. Step 6: Deploy Your Model.
Algorithm Selection Read the data. Create dependent and independent data sets based on our dependent and independent features. Split the data into training and testing sets. Train the model using different algorithms such as KNN, Decision tree, SVM, etc. Evaluate the classifier. Choose the classifier with the most accuracy.
The N-Gram algorithm To classify a text, the algorithm tallies each sequence of characters in the target text and infers to its model to see which frequency array is more relevant. You can use this model when you need to classify your text based on about sequence of characters that appears in a text.
1(a). 5 - Classification Problems in Real Life 1 - Email Spam. The goal is to predict whether an email is a spam and should be delivered to the Junk folder. 2 - Handwritten Digit Recognition. 3 - Image segmentation. 4 - Speech Recognition. 5 - DNA Expression Microarray. 6 - DNA Sequence Classification.

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