Classify company text easily

Aug 6th, 2022
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How to classify company text

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In this lecture, well introduce the topic of text classification and the Naive Bayes algorithm which is one of the most important ways of doing text classification. Lets begin by looking at some examples of text classification applications. Here Ive shown an email that I actually received the other day. How do I know that this email is spam? Take a look at the mail and think of some features you might automatically extract from this email that tells you that its spam. You might notice the word greats. A mis, a misspelling of great, so we have a typo here. Maybe you might notice important notice and maybe an exclamation point. Its pretty rare that universities put exclamation points in their subject headers. You might notice that theres no, Dan here, its not addressed to me in particular, and we have undisclosed recipients and theres no particular address. And the URLs a little funny here, thats not a Stanford URL. Maybe the word exciting. Each of these features can be combin

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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.
Linear Support Vector Machine is widely regarded as one of the best text classification algorithms.
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.
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.
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.
Some Examples of Text Classification: Sentiment Analysis. Language Detection. Fraud Profanity Online Abuse Detection. Detecting Trends in Customer Feedback. Urgency Detection in Customer Support.
Some Examples of Text Classification: Sentiment Analysis. Language Detection. Fraud Profanity Online Abuse Detection.
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.
Text classification also known as text tagging or text categorization is the process of categorizing text into organized groups. By using Natural Language Processing (NLP), text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content.
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.

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