Modeling and Prediction

Develop predictive models using topic models and word embeddings

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.

Functions

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Word and N-Gram Counting

`bagOfWords`	Bag-of-words model
`bagOfNgrams`	Bag-of-n-grams model
`addDocument`	Add documents to bag-of-words or bag-of-n-grams model
`removeDocument`	Remove documents from bag-of-words or bag-of-n-grams model
`removeInfrequentWords`	Remove words with low counts from bag-of-words model
`removeInfrequentNgrams`	Remove infrequently seen n-grams from bag-of-n-grams model
`removeWords`	Remove selected words from documents or bag-of-words model
`removeNgrams`	Remove n-grams from bag-of-n-grams model
`removeEmptyDocuments`	Remove empty documents from tokenized document array, bag-of-words model, or bag-of-n-grams model
`topkwords`	Most important words in bag-of-words model or LDA topic
`topkngrams`	Most frequent n-grams
`encode`	Encode documents as matrix of word or n-gram counts
`tfidf`	Term Frequency–Inverse Document Frequency (tf-idf) matrix
`join`	Combine multiple bag-of-words or bag-of-n-grams models

Sentiment Analysis

`vaderSentimentScores`	Sentiment scores with VADER algorithm
`ratioSentimentScores`	Sentiment scores with ratio rule

Word Embeddings and Encodings

`fastTextWordEmbedding`	Pretrained fastText word embedding
`wordEncoding`	Word encoding model to map words to indices and back
`doc2sequence`	Convert documents to sequences for deep learning
`wordEmbeddingLayer`	Word embedding layer for deep learning networks
`word2vec`	Map word to embedding vector
`word2ind`	Map word to encoding index
`vec2word`	Map embedding vector to word
`ind2word`	Map encoding index to word
`isVocabularyWord`	Test if word is member of word embedding or encoding
`readWordEmbedding`	Read word embedding from file
`trainWordEmbedding`	Train word embedding
`writeWordEmbedding`	Write word embedding file
`wordEmbedding`	Word embedding model to map words to vectors and back

Document Summarization and Similarity

`extractSummary`	Extract summary from documents
`bleuEvaluationScore`	Evaluate translation or summarization with BLEU similarity score
`rougeEvaluationScore`	Evaluate translation or summarization with ROUGE similarity score
`bm25Similarity`	Document similarities with BM25 algorithm
`cosineSimilarity`	Document similarities with cosine similarity
`textrankScores`	Document scoring with TextRank algorithm
`lexrankScores`	Document scoring with LexRank algorithm
`mmrScores`	Document scoring with Maximal Marginal Relevance (MMR) algorithm

Topic Modeling and Dimension Reduction

`fitlda`	Fit latent Dirichlet allocation (LDA) model
`fitlsa`	Fit LSA model
`resume`	Resume fitting LDA model
`logp`	Document log-probabilities and goodness of fit of LDA model
`predict`	Predict top LDA topics of documents
`transform`	Transform documents into lower-dimensional space
`ldaModel`	Latent Dirichlet allocation (LDA) model
`lsaModel`	Latent semantic analysis (LSA) model

Visualization

`wordcloud`	Create word cloud chart from text, bag-of-words model, bag-of-n-grams model, or LDA model
`textscatter`	2-D scatter plot of text
`textscatter3`	3-D scatter plot of text

Topics

Text Classification

Create Simple Text Model for Classification

This example shows how to train a simple text classifier on word frequency counts using a bag-of-words model.

Classify Text Data Using Deep Learning

This example shows how to classify text data using a deep learning long short-term memory (LSTM) network.

Classify Text Data Using Convolutional Neural Network

This example shows how to classify text data using a convolutional neural network.

Classify Out-of-Memory Text Data Using Deep Learning

This example shows how to classify out-of-memory text data with a deep learning network using a transformed datastore.

N-Gram Counting and Topic Modeling

Analyze Text Data Using Multiword Phrases

This example shows how to analyze text using n-gram frequency counts.

Analyze Text Data Using Topic Models

This example shows how to use the Latent Dirichlet Allocation (LDA) topic model to analyze text data.

Choose Number of Topics for LDA Model

This example shows how to decide on a suitable number of topics for a latent Dirichlet allocation (LDA) model.

Compare LDA Solvers

This example shows how to compare latent Dirichlet allocation (LDA) solvers by comparing the goodness of fit and the time taken to fit the model.

Sentiment Analysis

Create Simple Preprocessing Function

This example shows how to create a function which cleans and preprocesses text data for analysis.

Train a Sentiment Classifier

This example shows how to train a classifier for sentiment analysis using an annotated list of positive and negative sentiment words and a pretrained word embedding.