Text Analytics Toolbox


Text Analytics Toolbox

Analyze and model text data

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Import and Visualize Text Data

Extract text data from sources such as social media, news feeds, equipment logs, reports, and surveys.

Extract Text Data

Import text data into MATLAB® from single files or large collections of files, including PDF, HTML, and Microsoft® Word® and Excel® files.

Extracting text from a collection of Microsoft Word documents.

Visualize Text

Visually explore text datasets using word clouds and text scatter plots.

Word cloud showing the relative frequency of words using font size and color.

Language Support

Text Analytics Toolbox provides language specific preprocessing capabilities for English, Japanese, German, and Korean. Most functions also work with text in other languages.

Import, prepare, and analyze Japanese text.

Preprocess Text Data

Extract meaningful words from raw text.

Clean Text Data

Apply high-level filtering functions to remove extraneous content such as URLs, HTML tags, and punctuations, and correct spellings.

Simplify raw text (left) to work with the most meaningful words (right).

Filter Stop Words and Normalize Words to Root Form

Prioritize meaningful text data in your analysis by filtering out common words, words that appear too frequently or infrequently, and very long or very short words. Reduce the vocabulary and focus on the broader sense or sentiment of a document by stemming words to their root form or lemmatizing them to their dictionary form.

Removing stop words like “a” and “of” from documents.

Identify Tokens, Sentences, and Parts-of-Speech

Automatically split raw text into a collection of words using a tokenization algorithm. Add sentence boundaries, part-of-speech details, and other relevant information for context.

Adding part-of-speech and sentence details to tokenized documents.

Convert Text to Numeric Formats

Convert text data to numeric form for use in machine learning and deep learning.

Word and N-Gram Counting

Calculate word frequency statistics to represent text data numerically.

Identify and visualize the most frequently occurring words in a model.

Word Embedding and Encoding

Train word-embedding models such as word2vec continuous bag-of-words (CBOW) and skip-gram models. Import pretrained models including fastText and GloVe.

Visualize clusters in a text scatter plot using word embedding. 

Machine Learning with Text Data

Perform topic modeling, sentiment analysis, classification, dimensionality reduction, and document summary extraction using machine learning algorithms.

Topic Modeling

Discover and visualize underlying patterns, trends, and complex relationships in large sets of text data using machine learning algorithms such as latent Dirichlet allocation (LDA) and latent semantic analysis (LSA).

Identifying topics in storm report data.

Document Summarization and Keyword Extraction

Extract summary and relevant keywords from one or more documents automatically and evaluate similarity and importance of documents.

Extract summary from text.

Sentiment Analysis

Identify the attitudes and opinions expressed in text data to categorize statements as being positive, neutral, or negative. Build models that can predict sentiment in real time.

Identifying words that predict positive and negative sentiment.

Deep Learning with Text Data

Perform sentiment analysis, classification, summarization, and text generation using deep learning algorithms.

Transformer Models

Leverage transformer models such as BERT, FinBERT, and GPT-2 to perform transfer learning with text data for tasks such as sentiment analysis, classification, and summarization.

Transformer models for transfer learning with text data.

Text Classification

Classify text descriptions using word embeddings that can identify categories of text through deep learning.

Training a deep neural network to classify text data.

Text generation using Jane Austen’s Pride and Prejudice and a deep learning LSTM network.