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 Point the -g flag to other preprocessed feature files and the -f flag to the corresponding username-username-edges.txt file to run the label propagation on other datasets.
+The optional -e flag can be used to tell the label propagation algorithm which edges and/or seeds to include, according to the following abbreviations:
+* 'n' stands for the edges between n-grams and tweets that contain them.
+* 'f' stands for the follower graph
+* 'm' stands for seeds based on EmoMaxent's predictions
+* 'o' stands for the OpinionFinder/MPQA seeds on some unigrams
+* 'e' stands for emoticon seeds
+By default, all five of these are included, i.e. adding "-e nfmoe" to the above command line would not change output. To run on just the follower graph and EmoMaxent's predictions, for example, you would add "-e fm" to the command line, like so:
+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 -e fm
+You should see the following output:
+***** PER TWEET EVAL *****
+Accuracy: 0.8306011 (152.0/183)
+***** PER USER EVAL *****
+Number of users evaluated: 0 (min of 3 tweets per user)
+Mean squared error: NaN
 === Per-Target Evaluation ===
 Tweets in the HCR datasets are annotated for target as well as sentiment. To extract the list of targets for one of the HCR datasets (necessary to perform per-target evaluation), add a third argument before the '>' to the HCR preprocessing command, where that argument is a target output filename. For example, this will extract the targets from HCR-dev: