text_mining_slides_20180512

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1. Text Mining and Data Viz 2018-05-12 leoluyi@iii Slides http://pcse.pw/6WHWJ © leoluyi, 2018 1
2. 橕ෝ౯ 4 㸎瓽 Leo Lu 4 ݣय़ૡᓕ 4 ፓ獮ෝᰂᣟ禂๐率 4 Build data products 4 ETL 4 Models 4 Text mining 4 Viz 4 ... © leoluyi, 2018 2
3. Text Minning 窕纷 膏 ૡٍ㮉 © leoluyi, 2018 3
4. 膑碻դጱૡٍ vs. 碝Ӯդጱૡٍ © leoluyi, 2018 4
5. 犥獮౯㮉᮷አक़㾴Ո䌃ጱ䩚ᥜ tm + tmcn Rwordseg © leoluyi, 2018 5
6. ֕ฎ蝡犚ॺկஃஃࣁӾ෈ 䨝磪๚Ꭳጱ襊 © leoluyi, 2018 6
7. 犡ॠ౯㮉ᥝአӞ犚碝ጱૡٍ © leoluyi, 2018 7
8. 窕纷 Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model © leoluyi, 2018 8
9. Get data Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 9
10. PTT ฎ疌疌ጱঅ๏݋ Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 10
11. ྯॠ᮷磪盄ग़盄ग़ጱ䔂෈承碘 © leoluyi, 2018 11
12. ᛔ૩ጱ粖恝ᛔ૩䌃 devtools::install_packages( "leoluyi/PTTr") © leoluyi, 2018 12
13. Cleaning and preprocessing text ኸӥ虻懱牧݄ധ褾懱 © leoluyi, 2018 13
14. Tokenize Transform whole text into parts Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 14
15. For English 4 normalization 4 stemming (扃䓄൉玲) 4 lemmatization (扃ࣳ螭ܻ) 4 POS tagging 4 ... Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 15
16. Ӿ෈犲Ԓ穉斃墋㻌 4 䥁扃 4 犋䥁扃 4 POS tagging 4 ... Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 16
17. Semantic Parsing vs. Bag-of-Words © leoluyi, 2018 17
18. R tools 4 stringr 4 jiebaR Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 18
19. Embedding (Encode, Feature Extraction) Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 19
20. Embedding In a nutshell, Word Embedding turns text into numbers. 4 Embedding Layer1 4 Word2Vec 4 GloVe 4 doc2vec 4 sense2vec 1 https://machinelearningmastery.com/what-are-word-embeddings/ Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 20
21. © leoluyi, 2018 21
22. Demo Information Retrieval Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 22
23. Visualize 4 Dimension Reduction 4 t-sne 4 PCA 4 Clustering 4 Interactive or static plots Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 23
24. Visualize 4 tsne::tsne() 4 prcomp() Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 24
25. Model Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 25
26. Tasks 4 Classification 4 ෈๜獤觊 4 Clustering 4 ತ疨ፘ犲෈๜ 4 Generative models 4 ෈๜ᛔ㵕ኞ౮ Get data ➜ Tokenize ➜ Embedding ➜ Viz ➜ Model 26
27. አک磧盅᮷䨝మᥝ䌃ᛔ૩ጱ toolkit 4 Sparse Matrix manipulation 4 Informaiton retrieval tools 4 ... © leoluyi, 2018 27
28. Summary 1. Problem definition & specific goal: Get Curious About Text 2. Finding Your Data 3. Preprocessing Your Data 4 Removing stopwords, Stemming, Segmentation, ... 4. Feature Extraction 4 Document-Term Matrix: tm, text2vec 4 Named Entity Recognition, POS tagging 4 Word embeddings: word2vec, GloVe 5. More Text Mining Skills 4 sentiment analysis 4 topicmodels, LDAViz: LDA 6. More Than Words - Visualizing Your Results © leoluyi, 2018 28
29. 碍硁ᑀ䋊 㸎瓽 leoluyi@github https://leoluyi.github.io © leoluyi, 2018 29