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INTRODUCTION This project contains all the code necessary to reproduce the analysis presented in my R tutorial on mining Twitter for airline sentiment: http://jeffreybreen.wordpress.com/2011/07/04/twitter-text-mining-r-slides/ CONTENTS data/ opinion-lexicon-English/ - Hu & Liu's opinion lexicon negative-words.txt NLP-handbook-sentiment-analysis.pdf positive-words.txt source.txt source.webloc acsi.df.RData - airline customer satisfaction scores acsi.raw.df.RData - scraped from theacsi.org web site output/ - output files, mainly graphics qplot_delta_hist.pdf twitter_acsi_comparison_with_fit.pdf twitter_acsi_comparison.pdf twitter_score_histograms.pdf R/ - R source code 0_start.R 1_load.R 2_run.R scrape.R sentiment.R LICENSE - Copyright per Apache 2.0 license README - this file INSTRUCTIONS In order to run the analysis, start R from this project's root directory or it with setwd() 1. Load the prerequisite packages, our score.sentiment() function, and some environment variables with the "0_start.R" script: > source("R/0_start.R") 2. This distribution does not ship with any data from Twitter, so you will need to collect your own the first time you attempt to run this package. To collect data from Twitter, simply execute the "scrape.R" script: > source("R/scrape.R") This script caches your collected tweets to the data/ directory, so you only need to run this step once. 3. Load the Twitter data, opinion lexicon, and ACSI results from disk: > source("R/1_load.R") If this is your first time running this code, and you have not followed Step 2 to collect your own Twitter data, you will see this error message: Error: Tweets not found on disk -- source('R/scrape.R') to scrape Twitter first 4. Run the analysis: > source("R/2_run.R") Progress messages will be displayed on the console and all generated graphics will be displayed and saved as PDFs in the output/ directory. Jeffrey Oliver Breen firstname.lastname@example.org July 2011