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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
jbreen@cambridge.aero
July 2011
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