Text Mining with R for Social Science Research
- 6. Coreference resolution Question answering (QA) Part-of-speech (POS) tagging Word sense disambiguation (WSD) Paraphrase Named entity recognition (NER) Parsing Summarization Information extraction (IE) Machine translation (MT) Dialog Sentiment analysis mostly solved making good progress still really hard Spam detection (Classification) Let¡¯s go to Agra! Buy V1AGRA ¡ ? ? Colorless green ideas sleep furiously. ADJ ADJ NOUN VERB ADV Einstein met with UN officials in Princeton PERSON ORG LOC You¡¯re invited to our dinner party, Friday May 27 at 8:30 Party May 27 add Best roast chicken in San Francisco! The waiter ignored us for 20 minutes. Carter told Mubarak he shouldn¡¯t run again. I need new batteries for my mouse. The 13th Shanghai International Film Festival¡ µÚ13½ìÉϺ£¹ú¼ÊµçÓ°½Ú¿ªÄ»¡ The Dow Jones is up Housing prices rose Economy is good Q. How effective is ibuprofen in reducing fever in patients with acute febrile illness? I can see Alcatraz from the window! XYZ acquired ABC yesterday ABC has been taken over by XYZ Where is Citizen Kane playing in SF? Castro Theatre at 7:30. Do you want a ticket? The S&P500 jumped Source: Dan Jurafsky
- 7. non-standard English Great job @justinbieber! Were SOO PROUD of what youve accomplished! U taught us 2 #neversaynever & you yourself should never give up either? segmentation issues idioms dark horse get cold feet lose face throw in the towel neologisms unfriend Retweet bromance tricky entity names Where is A Bug¡¯s Life playing ¡ Let It Be was recorded ¡ ¡ a mutation on the for gene ¡ the New York-New Haven Railroad the New York-New Haven Railroad Source: Dan Jurafsky (modified) sarcasm A: I love Justin Bieber. Do you like him to? B:Yeah. Sure. I absolutely love him.
- 9. 1:10pm
- 11. Non-Stop
- 12. Adair Moesteller &Wallace Fung Collins et al
- 15. Tokenize Clean Stem Filter Then a hurricane came, and devastation reigned then a hurricane came and devastation reigned then a hurricane came and devastation reigned then a hurricane came and devastation reigned
- 16. GitHub site 1:20pm Code Lines: 1 - 49
- 18. Federalist Paper 1: Before Federalist Paper 1: After Code Lines: 71-88
- 19. Federalist Paper 1: After Code Lines: 89-104
- 26. Uncomment (CTRL + SHIFT +C) and run lines 107-139 Code Lines: 107-139 then rerun lines 141-206
- 27. 1:50pm - 2pm
- 36. This will take about 4 mins, depending on the computer you run it on Code Lines: 295-308
- 37. Source: David Blei (link to article)
- 39. Index.html file in the ¡°Federalist¡± folder in your working directory. Open with FireFox; it is not supported by Chrome or IE.
- 43. ? Na?ve Bayes predicts 9 of the 12 papers as written by Madison. ? K-NN predicts only 4 of the 12 papers as written by Madison ? Why? How stable are these results?? Code Lines: 371-373
- 44. 2:30pm
- 48. The Beige Book GitHub Source: Richard Heimann
- 61. First six records of BB.sentiment
- 62. First six records of BB.sentiment (updated)
- 63. Raw Scored Sentiment Scaled Scored Sentiment
- 66. Stanford Deep Learning NLP class materials
- 68. AlchemyAPI Taste Analytics Signals SAS Enterprise Miner SAS Sentiment Analysis Hamilton Soundtrack Amazon Reviews
- 69. R tm package Python nltk package Python gensim package Mallet
- 70. IntroductoryText MiningClass Coursera Natural Language ProcessingClass CourseraText Mining & Analytics Course Deep Learning for Natural Language Processing
- 71. https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part- 1-for-beginners-bag-of-words http://www.alchemyapi.com/developers/getting-started- guide/twitter-sentiment-analysis https://eight2late.wordpress.com/2015/09/29/a-gentle- introduction-to-topic-modeling-using-r/ http://www.r-bloggers.com/sentiment-analysis-on-donald- trump-using-r-and-tableau/ Follow this link for all R ¡°text¡± blogs on Rbloggers website