1.5. Text classification
Text classification is a natural language processing task in which text data is categorized into predefined labels or categories.
The basic goal of a text classification problem is to model one or several target variables conditioned on the input text data.
The objective here is to predict the category or label of a given text based on its content.
In text classification applications, the targets are usually discrete variables such as categories, labels, or sentiments.
Some examples are the following:
- Spam detection: Classifying emails as spam or not based on their content.
- Sentiment analysis: Classifying text data as positive, negative, or neutral based on the sentiment expressed.
- Topic classification: Classifying news articles into predefined topics such as politics, sports, technology, etc.
- Named entity recognition: Identifying and classifying named entities such as people, organizations, locations, etc.
Text data are noisy in their own way. Typos, grammatical errors, abbreviations and passages unrelated to the label add variation that says nothing about the category, while the useful information lies in recurring words, phrases and patterns.
The aim is a model that relies on those recurring patterns rather than on accidental details of the training documents, so that it also assigns the right category to texts it has never seen.
All model types
