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The Natural Language Processing Workshop
最新章節：
8. Sentiment Analysis
DoyouwanttolearnhowtocommunicatewithcomputersystemsusingNaturalLanguageProcessing(NLP)techniques,ormakeamachineunderstandhumansentiments?DoyouwanttobuildapplicationslikeSiri,Alexa,orchatbots,evenifyou’veneverdoneitbefore?WithTheNaturalLanguageProcessingWorkshop,youcanexpecttomakeconsistentprogressasabeginner,andgetuptospeedinaninteractiveway,withthehelpofhands-onactivitiesandfunexercises.ThebookstartswithanintroductiontoNLP.You’llstudydifferentapproachestoNLPtasks,andperformexercisesinPythontounderstandtheprocessofpreparingdatasetsforNLPmodels.Next,you’lluseadvancedNLPalgorithmsandvisualizationtechniquestocollectdatasetsfromopenwebsites,andtosummarizeandgeneraterandomtextfromadocument.Inthefinalchapters,you’lluseNLPtocreateachatbotthatdetectspositiveornegativesentimentintextdocumentssuchasmoviereviews.Bytheendofthisbook,you’llbeequippedwiththeessentialNLPtoolsandtechniquesyouneedtosolvecommonbusinessproblemsthatinvolveprocessingtext.
目錄(67章)
倒序
- 封面
- 版權信息
- Preface
- 1. Introduction to Natural Language Processing
- Introduction
- History of NLP
- Text Analytics and NLP
- Various Steps in NLP
- Word Sense Disambiguation
- Sentence Boundary Detection
- Kick Starting an NLP Project
- Summary
- 2. Feature Extraction Methods
- Introduction
- Types of Data
- Cleaning Text Data
- Feature Extraction from Texts
- Finding Text Similarity – Application of Feature Extraction
- Summary
- 3. Developing a Text Classifier
- Introduction
- Machine Learning
- Supervised Learning
- Developing a Text Classifier
- Building Pipelines for NLP Projects
- Saving and Loading Models
- Summary
- 4. Collecting Text Data with Web Scraping and APIs
- Introduction
- Collecting Data by Scraping Web Pages
- Dealing with Semi-Structured Data
- Summary
- 5. Topic Modeling
- Introduction
- Topic Discovery
- Topic-Modeling Algorithms
- Key Input Parameters for LSA Topic Modeling
- Hierarchical Dirichlet Process (HDP)
- Summary
- 6. Vector Representation
- Introduction
- What Is a Vector?
- Summary
- 7. Text Generation and Summarization
- Introduction
- Generating Text with Markov Chains
- Text Summarization
- Key Input Parameters for TextRank
- Recent Developments in Text Generation and Summarization
- Practical Challenges in Extractive Summarization
- Summary
- 8. Sentiment Analysis
- Introduction
- Tools Used for Sentiment Analysis
- The textblob library
- Understanding Data for Sentiment Analysis
- Training Sentiment Models
- Summary
- Appendix
- 1. Introduction to Natural Language Processing
- 2. Feature Extraction Methods
- 3. Developing a Text Classifier
- 4. Collecting Text Data with Web Scraping and APIs
- 5. Topic Modeling
- 6. Vector Representation
- 7. Text Generation and Summarization
- 8. Sentiment Analysis 更新時間：2021-06-11 18:39:41
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