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 Preprocessed 396 tweets. Fraction positive: 0.38636363
 }}}
 
-=== Syntax highlighting ===
+== Running the Experiments ==
 
-You can also highlight snippets of text, we use the excellent [[http://www.pygments.org/|Pygments]] library.
+=== LexRatio Baseline ===
 
-Here's an example of some Python code:
+To run LexRatio on the Stanford Sentiment dataset, use the following command from the UPDOWN_DIR directory:
 
 {{{
-#!python
-
-def wiki_rocks(text):
-	formatter = lambda t: "funky"+t
-	return formatter(text)
+$ updown lex-ratio -g data/stanford/stanford-features.txt -p src/main/resources/eng/lexicon/subjclueslen1polar.tff 
 }}}
 
-You can check out the source of this page to see how that's done, and make sure to bookmark [[http://pygments.org/docs/lexers/|the vast library of Pygment lexers]], we accept the 'short name' or the 'mimetype' of anything in there.
+You should see the following output:
+{{{
+***** PER TWEET EVAL *****
+58 tweets were abstained on; assuming half (29.0) were correct.
+Accuracy: 0.72131145 (132.0/183)
 
-Have fun!
+***** PER USER EVAL *****
+Number of users evaluated: 0 (min of 3 tweets per user)
+Mean squared error: NaN
+}}}
+
+Point the -g flag to other preprocessed feature files to run LexRatio on other datasets.
+
+=== EmoMaxent ===
+