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 Mean squared error: NaN
 }}}
 
-Point the -g flag to other preprocessed feature files to run EmoMaxent on other datasets. (Per-user evaluation makes the most sense on the HCR datasets.)
+Point the -g flag to other preprocessed feature files to run EmoMaxent on other datasets. (Per-user evaluation makes the most sense on the HCR datasets, where there are many users who have authored three or more tweets.)
+
+=== Label Propagation (Modified Adsorption) ===
+
+To run label propagation using [[http://code.google.com/p/junto/|Junto]]'s implementation of Modified Adsorption on the Stanford Sentiment dataset, use the following command:
+
+{{{
+$ updown 8 junto -g data/stanford/stanford-features.txt -m models/maxent-eng.mxm -p src/main/resources/eng/lexicon/subjclueslen1polar.tff -f data/stanford/username-username-edges.txt -r src/main/resources/eng/model/ngramProbs.ser.gz 
+}}}
+
+(Note that this currently requires more than 4 gigabytes of memory (the '8' above indicates that 8 are used) due to the way the unigram and bigram probabilities are stored. We plan on improving the space efficiency of this in the future. You can run the label propagation with less memory by eliminating the -r flag and its argument, but results will not be as good.)
+
+After some status output, you should see the following output:
+{{{
+***** PER TWEET EVAL *****
+Accuracy: 0.8469945 (155.0/183)
+
+***** PER USER EVAL *****
+Number of users evaluated: 0 (min of 3 tweets per user)
+Mean squared error: NaN
+}}}