book

Machine Learning Techniques for Text

Name: Machine Learning Techniques for Text
Author: Nikos Tsourakis
ISBN: 9781803242385

by Nikos Tsourakis

October 2022

Intermediate to advanced

448 pages

11h 3m

English

Packt Publishing

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Machine Learning Techniques for Text
AcknowledgmentsContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Chapter 1: Introducing Machine Learning for Text
The language phenomenonThe data explosionThe era of AIRelevant research fieldsThe machine learning paradigmTaxonomy of machine learning techniquesSupervised learningUnsupervised learningSemi-supervised learningReinforcement learningVisualization of the dataEvaluation of the resultsSummary
Chapter 2: Detecting Spam Emails
Technical requirementsUnderstanding spam detectionExplaining feature engineeringExtracting word representationsUsing label encodingUsing one-hot encodingUsing token count encodingUsing tf-idf encodingExecuting data preprocessingTokenizing the inputRemoving stop wordsStemming the wordsLemmatizing the wordsPerforming classificationGetting the dataCreating the train and test setsPreprocessing the dataExtracting the featuresIntroducing the Support Vector Machines algorithmUnderstanding Bayes’ theoremMeasuring classification performanceCalculating accuracyCalculating precision and recallCalculating the F-scoreCreating ROC and AUCCreating precision-recall curvesSummary
Chapter 3: Classifying Topics of Newsgroup Posts
Technical requirementsUnderstanding topic classificationPerforming exploratory data analysisExecuting dimensionality reductionUnderstanding principal component analysisUnderstanding linear discriminant analysisPutting PCA and LDA into actionIntroducing the k-nearest neighbors algorithmPerforming feature extractionPerforming cross-validationPerforming classificationComparison to the baseline modelIntroducing the random forest algorithmContracting a decision treePerforming classificationExtracting word embedding representationUnderstanding word embeddingPerforming vector arithmeticPerforming classificationUsing the fastText toolSummary
Chapter 4: Extracting Sentiments from Product Reviews
Technical requirementsUnderstanding sentiment analysisPerforming exploratory data analysisUsing the Software datasetExploiting the ratings of productsExtracting the word count of reviewsExploiting the helpfulness scoreIntroducing linear regressionPutting linear regression into actionIntroducing logistic regressionUnderstanding gradient descentUsing logistic regressionCreating training and test setsPerforming classificationApplying regularizationIntroducing deep neural networksUnderstanding logic gatesUnderstanding perceptronsUnderstanding artificial neuronsCreating artificial neural networksTraining artificial neural networksPerforming classificationSummary
Chapter 5: Recommending Music Titles
Technical requirementsUnderstanding recommender systemsPerforming exploratory data analysisCleaning the dataExtracting information from the dataUnderstanding the Pearson correlationIntroducing content-based filteringExtracting music recommendationsIntroducing collaborative filteringUsing memory-based collaborative recommendersApplying SVDClustering handwritten textApplying t-SNEUsing model-based collaborative systemsIntroducing autoencodersSummary
Chapter 6: Teaching Machines to Translate
Technical requirementsUnderstanding machine translationIntroducing rule-based machine translationUsing direct machine translationUsing transfer-based machine translationUsing interlingual machine translationIntroducing example-based machine translationIntroducing statistical machine translationModeling the translation problemCreating the modelsIntroducing sequence-to-sequence learningDeciphering the encoder/decoder architectureUnderstanding long short-term memory unitsPutting seq2seq in actionMeasuring translation performanceSummary
Chapter 7: Summarizing Wikipedia Articles
Technical requirementsUnderstanding text summarizationIntroducing web scrapingScraping popular quotesScraping book reviewsScraping Wikipedia articlesPerforming extractive summarizationPerforming abstractive summarizationIntroducing the attention mechanismIntroducing transformersPutting the transformer into actionMeasuring summarization performanceSummary
Chapter 8: Detecting Hateful and Offensive Language
Technical requirementsIntroducing social networksUnderstanding BERTPre-training phaseFine-tuning phasePutting BERT into actionIntroducing boosting algorithmsUnderstanding AdaBoostUnderstanding gradient boostingUnderstanding XGBoostCreating validation setsLearning the myth of IcarusExtracting the datasetsTreating imbalanced datasetsClassifying with BERTTraining the classifierApplying early stoppingUnderstanding CNNAdding pooling layersIncluding CNN layersSummary

Chapter 9: Generating Text in Chatbots
Technical requirementsUnderstanding text generationCreating a retrieval-based chatbotUnderstanding language modelingUnderstanding perplexityBuilding a language modelCreating a generative chatbotUsing a pre-trained modelCreating the GUICreating the web chatbotFine-tuning a pre-trained modelSummary
Chapter 10: Clustering Speech-to-Text Transcriptions
Technical requirementsUnderstanding text clusteringPreprocessing the dataUsing speech-to-textIntroducing the K-means algorithmPutting K-means into actionIntroducing DBSCANPutting DBSCAN into actionAssessing DBSCANIntroducing the hierarchical clustering algorithmPutting hierarchical clustering into actionIntroducing the LDA algorithmPutting LDA into actionSummary
Index
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Content preview from Machine Learning Techniques for Text

2 Detecting Spam Emails

Electronic mail is a ubiquitous internet service for exchanging messages between people. A typical problem in this sphere of communication is identifying and blocking unsolicited and unwanted messages. Spam detectors undertake part of this role; ideally, they should not let spam escape uncaught while not obstructing any non-spam.

This chapter deals with this problem from a machine learning (ML) perspective and unfolds as a series of steps for developing and evaluating a typical spam detector. First, we elaborate on the limitations of performing spam detection using traditional programming. Next, we introduce the basic techniques for text representation and preprocessing. Finally, we implement two classifiers using an ...

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ISBN: 9781803242385

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Machine Learning Techniques for Text

by Nikos Tsourakis

2

Detecting Spam Emails

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.