To find clusters and extract features from high-dimensional text datasets, you can use machine learning techniques and models such as LSA, LDA, and word embeddings. You can combine features created with Text Analytics Toolbox™ with features from other data sources. With these features, you can build machine learning models that take advantage of textual, numeric, and other types of data.
This example shows how to train a simple text classifier on word frequency counts using a bag-of-words model.
This example shows how to use the Latent Dirichlet Allocation (LDA) topic model to analyze text data.
This example shows how to classify the event type of weather reports from their text descriptions using a deep learning long short-term memory (LSTM) network.
This example shows how to visualize word embeddings using 2-D and 3-D t-SNE and text scatter plots.