MATLAB Text Analytics Toolbox User’s Guide

MATLAB Text Analytics Toolbox User’s Guide
اسم المؤلف
MathWorks
التاريخ
29 أغسطس 2022
المشاهدات
400
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MATLAB Text Analytics Toolbox User’s Guide
MathWorks
Contents
Text Data Preparation
1
Extract Text Data from Files . 1-2
Prepare Text Data for Analysis 1-11
Parse HTML and Extract Text Content . 1-18
Correct Spelling in Documents . 1-22
Create Extension Dictionary for Spelling Correction 1-24
Create Custom Spelling Correction Function Using Edit Distance
Searchers . 1-28
Data Sets for Text Analytics 1-34
Modeling and Prediction
2
Create Simple Text Model for Classification 2-2
Analyze Text Data Using Multiword Phrases 2-7
Analyze Text Data Using Topic Models 2-13
Choose Number of Topics for LDA Model . 2-19
Compare LDA Solvers . 2-23
Visualize LDA Topics Using Word Clouds . 2-28
Visualize LDA Topic Probabilities of Documents . 2-30
Visualize Document Clusters Using LDA Model 2-35
Visualize LDA Topic Correlations 2-38
Visualize Correlations Between LDA Topics and Document Labels . 2-42
Create Co-occurrence Network . 2-48
iii
ContentsAnalyze Text Data Containing Emojis 2-52
Analyze Sentiment in Text 2-58
Generate Domain Specific Sentiment Lexicon . 2-61
Train a Sentiment Classifier 2-71
Extract Keywords from Text Data Using RAKE . 2-79
Extract Keywords from Text Data Using TextRank 2-82
Classify Text Data Using Deep Learning 2-85
Classify Text Data Using Convolutional Neural Network . 2-93
Classify Text Data Using Custom Training Loop . 2-100
Multilabel Text Classification Using Deep Learning 2-111
Sequence-to-Sequence Translation Using Attention . 2-130
Language Translation Using Deep Learning 2-146
Classify Out-of-Memory Text Data Using Deep Learning 2-168
Pride and Prejudice and MATLAB 2-174
Word-By-Word Text Generation Using Deep Learning 2-180
Generate Text Using Autoencoders . 2-186
Define Text Encoder Model Function 2-198
Define Text Decoder Model Function 2-205
Classify Out-of-Memory Text Data Using Custom Mini-Batch Datastore
2-212
Display and Presentation
3
Visualize Text Data Using Word Clouds 3-2
Visualize Word Embeddings Using Text Scatter Plots 3-8
iv ContentsLanguage Support
4
Language Considerations . 4-2
Language-Independent Features 4-4
Japanese Language Support . 4-6
Tokenization . 4-6
Part of Speech Details 4-6
Named Entity Recognition 4-7
Stop Words 4-8
Lemmatization . 4-9
Language-Independent Features 4-9
Analyze Japanese Text Data 4-11
German Language Support . 4-21
Tokenization 4-21
Sentence Detection 4-21
Part of Speech Details 4-22
Named Entity Recognition . 4-23
Stop Words . 4-24
Stemming 4-24
Language-Independent Features . 4-25
Analyze German Text Data . 4-26
Korean Language Support . 4-37
Tokenization 4-37
Part of Speech Details 4-37
Named Entity Recognition . 4-37
Stop Words . 4-37
Lemmatization 4-37
Language-Independent Features . 4-37
Language-Independent Features 4-39
Word and N-Gram Counting 4-39
Modeling and Prediction . 4-39
Glossary
5
Text Analytics Glossary . 5-2
Documents and Tokens . 5-2
Preprocessing 5-3
Modeling and Prediction 5-3
Visualization . 5-5

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