Evaluation

The evaluation will be based on macro average F1 measure. For development purposes, we provide an evaluation script which you can download at

The script takes two files as input, a gold file (which is a file like trial.labels in the data section) and a prediction file which has the same format.

This is only to help with the evaluation during development. Participation is managed through our Codalab page at https://competitions.codalab.org/competitions/19214.

To help you estimate the performance, a baseline result with a bag-of-words (unigrams and bigram) logistic regression classifier, trained on the whole training data (version 3) and tested on the whole trial data (version 3) lead to the following result:

Label TP FP FN P R F
joy 1197 536 539 0.691 0.690 0.690
sad 801 556 659 0.59 0.549 0.569
disgust 972 634 625 0.605 0.609 0.607
anger 826 712 774 0.537 0.516 0.526
surprise 953 744 647 0.562 0.596 0.578
fear 1036 624 562 0.624 0.648 0.636
MicAvg 5785 3806 3806 0.603 0.603 0.603
MacAvg 0.601 0.601 0.601

Official result: 0.601042964046

For comparison, these were the results for the data of version 2.

Label TP FP FN P R F
joy 975 653 622 0.599 0.611 0.605
sad 821 646 778 0.56 0.513 0.536
disgust 970 642 627 0.602 0.607 0.605
anger 818 705 782 0.537 0.511 0.524
surprise 952 747 648 0.56 0.595 0.577
fear 1038 624 560 0.625 0.65 0.637
MicAvg 5574 4017 4017 0.581 0.581 0.581
MacAvg 0.58 0.581 0.58

Official result: 0.580426957687

For comparison, these were the results for the data of version 1.

Label TP FP FN P R F
joy 979 656 621 0.599 0.612 0.605
sad 817 644 783 0.559 0.511 0.534
disgust 975 643 625 0.603 0.609 0.606
anger 818 708 782 0.536 0.511 0.523
surprise 957 739 643 0.564 0.598 0.581
fear 1038 626 562 0.624 0.649 0.636
MicAvg 5584 4016 4016 0.582 0.582 0.582
MacAvg 0.581 0.582 0.581

Official result: 0.580853294

This result have been achieved with the simple text classifier (a wrapper for liblinear at https://bitbucket.org/rklinger/simpletextclassifier), tested on Mac OS X and Linux with Java 1.8:

# get and compile simple text classifier baseline
unset SSH_ASKPASS
git clone https://bitbucket.org/rklinger/simpletextclassifier.git
cd simpletextclassifier
mvn compile assembly:single
cd ..

# get training data (you need your credentials here)
wget --user USERNAME --password PASSWORD http://implicitemotions.wassa2018.com/data/protected/train-v3.csv.gz
gunzip train-v3.csv.gz

# get trial data
wget http://implicitemotions.wassa2018.com/data/unprotected/trial-v3.csv.gz
wget http://implicitemotions.wassa2018.com/data/unprotected/trial-v3.labels.gz
gunzip trial-v3.csv.gz
gunzip trial-v3.labels.gz

# get official evaluation script
wget http://implicitemotions.wassa2018.com/evaluation/evaluate-iest.py
chmod +x evaluate-iest.py

# train model
./simpletextclassifier/bin/run.sh --train ./train-v3.csv --model ./iest.model

# apply model
./simpletextclassifier/bin/run.sh --test ./trial-v3.csv --model ./iest.model > ./trial-v3.prediction

# evaluate
./evaluate-iest.py trial-v2.labels trial-v3.prediction