Text Analytics Toolbox™ provides algorithms and visualizations for preprocessing, analyzing, and modeling text data. Models created with the toolbox can be used in applications such as sentiment analysis, predictive maintenance, and topic modeling.
Text Analytics Toolbox includes tools for processing raw text from sources such as equipment logs, news feeds, surveys, operator reports, and social media. You can extract text from popular file formats, preprocess raw text, extract individual words, convert text into numerical representations, and build statistical models.
Using machine learning techniques such as LSA, LDA, and word embeddings, you can find clusters and create features from high-dimensional text datasets. Features created with Text Analytics Toolbox can be combined with features from other data sources to build machine learning models that take advantage of textual, numeric, and other types of data.
This example shows how to use text analytics to classify text data using only 10 lines of MATLAB® code.
Import text data from different sources
This example shows how to create a function which cleans and preprocesses text data for analysis.
This example shows how to fit a topic model to text data and visualize the topics.
This example shows how to visualize text data using word clouds.
Glossary of text analytics terms.
Text Analytics Toolbox Overview
Analyze and model text data with Text Analytics Toolbox