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SMAC LAB Social Media Text Analysis
- 1. SMAC LAB, LSU OCT 19, 2018 SMAC Talks Telling Stories from Social Media Text 1 Instructor: Dr. Ke (Jenny) Jiang
- 2. Telling Stories from Social Media Text 1 Collect Text Data Clean Text Text Analysis Visualization Python, TCAT, Crimson Hexagon… Remove Stop Words, Stemming Replacing “/”, “@” and “|” with space Convert the text to lower case Remove punctuations Frequency Analysis, Sentiment Analysis Entity Detection, Topic Modeling Word Clouds, Semantic Network Analysis R, Gephi Tell a Story
- 3. Step 1: Collect Text Data Using Crimson Hexagon Log in Crimson Hexagon
- 4. Step 1: Collect Text Data Using Crimson Hexagon Go to Forsight
- 5. Step 1: Collect Text Data Using Crimson Hexagon Click @realDonaldTrump
- 6. Step 1: Collect Text Data Using Crimson Hexagon Manage — Bulk Export
- 7. Step 1: Collect Text Data Using Crimson Hexagon Export 10,000 posts
- 8. Step 1: Collect Text Data Using Crimson Hexagon Open exported file — Save as trump.csv
- 9. Step 1: Collect Text Data Using Crimson Hexagon Open trump.csv — Delete the first line
- 10. Step 2: Create/Load a text file a. Set Working Directory — Directory for trump.csv
- 11. b. Get a Sample Step 3: Create/Load a Text File
- 12. Step 4: Install and load the Required Packages
- 13. Create a text Corpus Step 5: Load the Data as a Corpus Output
- 14. Replacing “/”, “@” and “|” with space Step 6: Clean Text Output
- 15. Convert the text to lower case Step 6: Clean Text Output
- 16. Remove numbers Step 6: Clean Text Output
- 17. Remove english common stop-words Step 6: Clean Text Output
- 18. Remove english common stop-words Text Bag of Words Clean Step 6: Clean Text
- 19. Remove your own stop word Step 6: Clean Text Output
- 20. Remove punctuations Step 6: Clean Text Output
- 21. Eliminate Extra White Spaces Step 6: Clean Text Output
- 22. Stemming Step 6: Clean Text Output
- 23. Stemming Loves Loved Loving Love s Love d Love ing Love Step 6: Clean Text Stemming
- 24. Frequency Step 7: Build a Term-Document Matrix Output
- 25. Frequency Visualization Step 7: Build a Term-Document Matrix Output
- 26. Generate the Word cloud Step 8: Build a Term-Document Matrix Output
- 27. Step 9: Save Text Corpus to .csv
- 28. Step 10: Download and Open ConText http://context.ischool.illinois.edu/download.php#download
- 29. Preparation Creating two folders: raw data, results move the trumpsemiclean.csv to the raw data folder
- 30. Parse CSV Files ConText Tutorials
- 31. Parse CSV Files ConText Tutorials
- 32. Parse CSV Files ConText Tutorials
- 33. Parse CSV Files ConText Tutorials
- 34. Parse CSV Files ConText Tutorials
- 35. Parse CSV Files ConText Tutorials
- 36. Parse CSV Files ConText Tutorials
- 37. Parse CSV Files ConText Tutorials
- 65. Word Clouds of Topics and Sentiment ConText Tutorials
- 66. Word Clouds of Topics and Sentiment ConText Tutorials
- 67. Word Clouds of Topics and Sentiment ConText Tutorials
- 68. Word Clouds of Topics and Sentiment ConText Tutorials
- 69. Word Clouds of Topics and Sentiment ConText Tutorials
- 70. Word Clouds of Topics and Sentiment ConText Tutorials
- 71. Word Clouds of Topics and Sentiment ConText Tutorials
- 72. Word Clouds of Topics and Sentiment ConText Tutorials
- 73. Word Clouds of Topics and Sentiment ConText Tutorials