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.
Text preprocessing and normalization
Machine learning algorithms, including latent Dirichlet allocation (LDA) and latent semantic analysis (LSA)
Word-embedding training, and pretrained model import from word2vec, FastText, and GloVe
Word cloud and text scatter plots
Document import from PDF and Microsoft® Word files
TF-IDF and word frequency statistics