Types of text classification
Text classification is an NLP task where ML algorithms assign predefined categories or labels to text based on its content. It involves training a model on a labeled dataset to enable it to accurately predict the category of unseen or new text inputs. Text classification methods can be categorized into three main types – supervised learning, unsupervised learning, and semi-supervised learning:
- Supervised learning: This type of text classification involves training a model on labeled data, where each data point is associated with a target label or category. The model then uses this labeled data to learn the patterns and relationships between the input text and the target labels. Examples of supervised learning algorithms for text classification include naive bayes, SVMs, and neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
- Unsupervised learning: This type of text classification involves...
