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EmoInt

Scripts for for the WASSA-2017 Shared Task on Emotion Intensity (EmoInt).

1. Evaluation Script

The evaluation script evaluate.py calculates the following four measures between the gold standard scores and the given predictions.

  1. Pearson_0_1: corresponds to the Pearson correlation coefficient caclualted between all the predicions and the gold instances.
  2. Spearman_0_1: corresponds to the Spearman's rank correlation coefficient caclualted between all the predicions and the gold instances.
  3. Pearson_05_1: corresponds to the Pearson correlation between predictions and gold instances where the gold score is greater than or equal to 0.5.
  4. Separman_05_1: corresponds to the Spearman correlation between predictions and gold instances where the gold score is greater than or equal to 0.5.

The script can receive multiple pairs of prediction and gold standard files. In case of receiving more than one pair, it will compute the average Pearson and Spearman correlation. Note that the average Pearson_0_1 obtained for the four emotions (anger, fear, joy and sadness) is the bottom-line competition metric and that remainder metrics are only given as a reference.

1.1. Prerequisites

1.2. Usage

python evaluate.py <number-of-pairs> <file-prediction-1> <file-gold-1> ..... <file-prediction-n> <file-gold-n>

Example:

python evaluate.py 4 anger-pred.txt anger-gold.txt fear-pred.txt fear-gold.txt joy-pred.txt joy-gold.txt sadness-pred.txt sadness-gold.txt

1.3. Format

Each input file must have the following format: id[tab]tweet[tab]emotion[tab]score. The script will complain in case of receiving a file with wrong format.

If you want to use the script purely a format checker, evaluate your predictions against themselves:

python evaluate.py 1 anger-pred.txt anger-pred.txt

2. Weka Baseline System

We have implemented a WEKA package called AffectiveTweets to be used as a baseline system. The package allows calculating multiple features from a tweet. Installation instructions are given in the project's webpage. Make sure to install the LibLinear and RankCorrelation packages before running the baselines.

We have also implemented the tweets_to_arff.py script for converting the task data into arff format, and the fix_weka_output.py script for converting weka predictions into the official submission format.

2.1. Example

  1. Convert training and target data for the anger emotion into arff format:
python tweets_to_arff.py data/anger-ratings-0to1.train.txt data/anger-ratings-0to1.train.arff
python tweets_to_arff.py data/anger-ratings-0to1.test.target.txt data/anger-ratings-0to1.test.target.arff

If testing data hasn't been provided yet, you can split the training file into training and testing sub-samples.

  1. Train an SVM regression (from LibLinear) on the training data using lexicons, SentiStrength, and word embeddings as features, classify the target tweets, and output the predictions:
java -Xmx4G -cp $HOME/weka-3-8-1/weka.jar weka.Run weka.classifiers.meta.FilteredClassifier -t data/anger-ratings-0to1.train.arff -T data/anger-ratings-0to1.test.target.arff -classifications "weka.classifiers.evaluation.output.prediction.CSV -use-tab -p first-last -file data/anger-pred.csv" -F "weka.filters.MultiFilter -F \"weka.filters.unsupervised.attribute.TweetToEmbeddingsFeatureVector -I 2 -B $HOME/wekafiles/packages/AffectiveTweets/resources/w2v.twitter.edinburgh.100d.csv.gz -S 0 -K 15 -L -O\" -F \"weka.filters.unsupervised.attribute.TweetToLexiconFeatureVector -I 2 -A -D -F -H -J -L -N -P -Q -R -T -U -O\" -F \"weka.filters.unsupervised.attribute.TweetToSentiStrengthFeatureVector -I 2 -U -O\" -F \"weka.filters.unsupervised.attribute.Reorder -R 5-last,4\"" -W weka.classifiers.functions.LibLINEAR -- -S 12 -C 1.0 -E 0.001 -B 1.0 -L 0.1 -I 1000

Make sure that the LibLinear Weka package has been properly installed.

  1. Convert the predictions into the task format:
python fix_weka_output.py data/anger-pred.txt data/anger-pred.txt
  1. Evaluate the predictions:
python evaluate.py 1 data/anger-pred.txt data/anger-ratings-0to1.test.gold.txt

2.2. Another example

Next, we show another example using all the features supported by the baseline system: word n-grams, character n-grams, Brown word clusters, POS tags, lexicons, SentiStrengh, and word embeddings. In this case we are using word embeddings trained from a large corpus of tweets that can be downloaded here. Replace the step 2 from the previous example by the following command:

java -Xmx8G -cp $HOME/weka-3-8-1/weka.jar weka.Run weka.classifiers.meta.FilteredClassifier -t data/anger-ratings-0to1.train.arff -T data/anger-ratings-0to1.test.target.arff -classifications "weka.classifiers.evaluation.output.prediction.CSV -use-tab -p first-last -file data/anger-pred.csv" -F "weka.filters.MultiFilter -F  \"weka.filters.unsupervised.attribute.TweetToSparseFeatureVector -M 2 -I 3 -R -Q 3 -A -D 3 -E 5 -L -O -F -G 2 -I 2\" -F \"weka.filters.unsupervised.attribute.TweetToEmbeddingsFeatureVector -I 2 -B $HOME/wekafiles/packages/AffectiveTweets/resources/w2v.twitter.edinburgh10M.400d.csv.gz -S 0 -K 15 -L -O\" -F \"weka.filters.unsupervised.attribute.TweetToLexiconFeatureVector -I 2 -A -D -F -H -J -L -N -P -Q -R -T -U -O\" -F \"weka.filters.unsupervised.attribute.TweetToSentiStrengthFeatureVector -I 2 -U -O\" -F \"weka.filters.unsupervised.attribute.Reorder -R 5-last,4\"" -W weka.classifiers.functions.LibLINEAR -- -S 12 -C 1.0 -E 0.001 -B 1.0 -L 0.1 -I 1000

2.3 Cross-validation

There is also possible to obtain cross-validated performance results using Weka.

  • In the following example, we train an SVM regression using lexicon features and we omit the testing file (-T) for obtaining cross-validated results:
java -Xmx4G -cp $HOME/weka-3-8-1/weka.jar weka.Run weka.classifiers.meta.FilteredClassifier -t data/anger-ratings-0to1.train.arff  -F "weka.filters.MultiFilter -F \"weka.filters.unsupervised.attribute.TweetToLexiconFeatureVector -I 2 -A -D -F -H -J -L -N -P -Q -R -T -U -O\" -F \"weka.filters.unsupervised.attribute.Reorder -R 5-last,4\"" -W weka.classifiers.functions.LibLINEAR -- -S 12 -C 1.0 -E 0.001 -B 1.0 -L 0.1 -I 1000

3. CodaLab

A valid submission file for Codalab is a zip compressed file containing the following files:

  1. anger-pred.txt
  2. fear-pred.txt
  3. joy-pred.txt
  4. sadness-pred.txt

Each of these files must follow the official submission format. An example of submission file for the development data can be downloaded here. The predictions were obtained using the Weka baseline system with the configuration used in the example from Section 2.2. A reference bash code for building this submission file from scratch is provided here.