Learn to classify text and documents using IAB-2 taxonomy
The IAB-2 (Interactive Advertising Bureau) taxonomy is a system of classifying digital content into organized categories. This system is used by many businesses, including publishers, advertisers, and marketers, to manage digital content and make it easier to find and use it. It is a two-level classification system that is designed to help businesses quickly and accurately arrange digital content into specific categories. It makes it easier for marketers to pitch their messages to the right audiences. GroupDocs.Classification for .NET API supports IAB-2 taxonomy and you can efficiently classify text and PDF, Microsoft Word, OpenDocument, RTF, and Text documents using this taxonomy.
Develop text classification apps with IAB-2 taxonomy in .NET
To classify text in .NET, please use the following C# code:
/*
* Text classifier with IAB-2 Taxonomy using C#
*/
Classifier classifier = new Classifier();
string statement = "Medicine is an important part of our lives";
var response = classifier.Classify(statement, 3, Taxonomy.Iab2);
response.BestResults.ToList().ForEach(bestResult => Console.WriteLine($"Class: {bestResult.Name}, \tProbability: {bestResult.Probability}"));
Perform document classification in .NET using IAB-2 taxonomy
The following code shows how to do document analysis in .NET:/* * Document analysis and classification (PDF, Word, ODT, RTF, TXT) with IAB-2 Taxonomy using C# */ Classifier classifier = new Classifier(); var filename = "document.pdf"; var response = classifier.Classify(filename, "" , 4, Taxonomy.Iab2); response.BestResults.ToList().ForEach(bestResult => Console.WriteLine($"Class: {bestResult.Name}, \t Probability: {bestResult.Probability}"));
Classification of text and data using document taxonomy in .NET
Document taxonomy is the process of organizing different types of documents into meaningful categories. It is used to classify digital content enabling users to search and retrieve the desired information quickly and accurately. Using this system, you can classify the text and files into several categories such as financial and legal documents, reports, emails, forms, and other digital media. Additionally, document taxonomy could provide context for your search engine optimization (SEO) efforts too. Using GroupDocs.Classification for .NET API, you can classify popular data files including PDF, DOC, DOCX, DOCM, DOT, DOTX, RTF, ODT, OTT, and TXT.
How to perform content classification using document taxonomy in .NET?
The following C# code will let you classify text using document taxonomy:
/*
* Classify Text with Document Taxonomy using C#
*/
Classifier classifier = new Classifier();
string statement = "Sooner or later technology will overcome labor work";
var response = classifier.Classify(statement, 2, Taxonomy.Documents);
response.BestResults.ToList().ForEach(bestResult => Console.WriteLine($"Class: {bestResult.Name}, \tProbability: {bestResult.Probability}"));
Perform document analysis using document taxonomy in the .NET platform
Please use the below-given C# code to classify documents in .NET:
/*
* Document analysis and classification (PDF, Word, ODT, RTF, TXT) with Document Taxonomy using C#
*/
Classifier classifier = new Classifier();
var filename = "document.pdf";
var response = classifier.Classify(filename, "" , 4, Taxonomy.Documents);
response.BestResults.ToList().ForEach(bestResult => Console.WriteLine($"Class: {bestResult.Name}, \t Probability: {bestResult.Probability}"));
Using sentiment analysis taxonomy for classification of text in .NET
Sentiment analysis is a method of organizing digital content and other data to measure the sentiment of customers and other stakeholders. It is used to analyze and quantify the attitude of a person or group toward a particular topic or issue. By arranging sentiment into positive, negative, and neutral categories, it becomes possible to identify trends and patterns in the sentimentality of digital content, which in turn helps to observe customer feedback and behavior, evaluate customer satisfaction, and take informed decisions on product development and marketing strategies. You can make use of the sentiment analysis taxonomy when classifying documents with GroupDocs.Classification for .NET API and automate the process of text analysis.
Analyze text with sentiment analysis in .NET
For evaluating the tone of text with sentiment analysis, please use this C# code:
// Analyze the positivity of text using sentiment classifier in C#.
var sentiment = "Experience is simply the name we give our mistakes";
var sentimentClassifier = new SentimentClassifier();
/// PositiveProbability method returns the positive probability of the sentiment.
var positiveProbability = sentimentClassifier.PositiveProbability(sentiment);
Console.WriteLine($"Positive Probability of the sentiment { positiveProbability }");
Classifying a group of texts with sentiment analysis
To classify more than one comment, please utilize the following code snippet:
// Insert the comments to classify in a string array
var sentiments = new string [ ] {
"Now that is out of the way, this thing is a beast. It is fast and runs cool.",
"Experience is simply the name we give our mistakes",
"When I used compressed air a cloud of dust bellowed out from the card (small scuffs and scratches).",
"This is Pathetic."
};
var classifier = new GroupDocs.Classification.SentimentClassifier();
var sentimentPositivity = sentiments.Select(x => classifier.PositiveProbability(x)).ToArray();
Console.WriteLine(string.Join("\\n", sentimentPositivity));
Please feel free to view our fully working GitHub-hosted API code examples. And if you want to classify text and PDF, DOCX, DOC, ODT, DOTX, DOCM, RTF, and TXT files from your mobile, tablet, or desktop PC, please use our Free Online Document and Text Classification Apps.
