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Sentiment Classification - Computational Intelligence Lab 2020

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Description

This is a project that was done in the Computational Intelligence Lab 2020 at ETH Zurich (see Course website). Specifically we are doing sentiment analysis of tweets and classify them into positive and negative sentiments (see Kaggle competition). Our team name was FrontRowCrew and we were eventually ranked first out of 35 teams on the private test set.

Setup

The experiments were run and tested with Python version 3.7.1.

Download data from: http://www.da.inf.ethz.ch/teaching/2018/CIL/material/exercise/twitter-datasets.zip

clone project

git clone https://github.com/ferraric/Computational-Intelligence-Lab-2020.git

install project

cd Computational-Intelligence-Lab-2020

move data into the data folder

mkdir data
mv path-to-downloaded-folder/twitter-datasets/* data

before running make sure that the source directory is recognized by your PYTHONPATH, for example do:

export PYTHONPATH=/path_to_source_directory/Computational-Intelligence-Lab-2020:$PYTHONPATH

If on Leonhard: install your python virtual environment into Computational-Intelligence-Lab-2020/venv

python3 -m venv ~/Computational-Intelligence-Lab-2020/venv
source ./init_leonhard.sh

If local:

pip install -r requirements.txt

Note that the pytorch version we used (pre-compiled with a specific cuda version) is not available for macOS. If you want to run it locally on a mac, change the pytorch version in the requirement file to the following:

torch==1.5.0

Reproduce Experiments

To log the experiment results we used Comet (https://www.comet.ml/docs/), a tensorboard like logger. Unfortunately we cannot make access to our experiment logs public. However, if access to the logs is needed, contact jeremyalain.scheurer@gmail.com. If you would like to use your own comet account to run the experiments, fill the comet related config options with your account credentials.

All experiments can be run with the config option "use_comet_experiments": false, which is the default option. In that case, the logs and saved predictions are found in the same directory where the model checkpoint is saved. That path is the concatenation of the config option "model_save_directory", the config option "experiment_name" and the timestamp of execution start time. Example: experiments/bert-baseline/20-07-25_12-25-02/

To calculate the accuracy of a prediction file, run the following command:

python utilities/calculate_accuracy.py -p path-to-predictions.csv -l path-to-labels.csv

Note that the predictions should be formatted as specified in the sample_submission.csv file.

The following holds for all models except Google Natural Language API and GloVe:

The hyperparameters epochs, max_tokens_per_tweet, validation_size, validation_split_random_seed, batch_size, n_data_loader_workers, learning_rate were unchanged for all runs with a particular model. How other hyperparameters were varied is described in the following sections.

When running an experiment, at the end of training, the provided test data are automatically predicted with the best saved checkpoint. If one needs to predict a set of tweets from an existing checkpoint, one needs to point the config option "test_tweets_path" to the corresponding tweets and provide the model checkpoint via the argument -t. Example:

python mains/bert.py -c configs/bert.json -t path-to-model-checkpoint/model_checkpoint.ckpt

Baselines

Google Natural Language API

Note that one needs a Google cloud account and credits to use this service. Make sure you set the variable GOOGLE_APPLICATION_CREDENTIALS to point to the json file containing your account credentials. Also as a disclaimer we want to note that the Google Natural Language API is a service that costs money. One usually has a certain amount of free calls to the API (which we used). But we want to remind you to first check out what your free API call limitations are.

export GOOGLE_APPLICATION_CREDENTIALS=path-to-your-account-credentials.json

python baselines/google_nlp_api.py -c configs/google_nlp_api.json

GloVe

To run the experiments for GloVe, download the code from https://github.com/dalab/lecture_cil_public/tree/master/exercises/2019/ex6 to generate the vocabulary and training the word embeddings.

In the README of that page, the instructions are given to build the vocabulary and the co-occurrence matrix for training. Note that modifications have to be made to build_vocab.sh and cooc.py to use the full dataset.

After building the co-occurrence matrix, run the training of the word embeddings by executing

python glove_solution.py

In that script, the number of epochs can be specified.

After this is done, move both the generated vocab.pkl and the embeddings.npz files to the data folder defined in the Setup section.

To train a classifier using the GloVe embeddings, run:

python baselines/main_glove.py -c configs/glove_embeddings_{logregression, decisiontree, randomforest}_classifier.json

The grid search parameters can be modified inside the respective config files.

BERT

python mains/bert.py -c configs/bert.json

The scores provided in the report were the average across runs with 5 different random seeds. The random seeds we used were [0, 1, 2, 3, 4], they were set via the config option random_seed.

BERTweet

For the ablation study, we ran 3 models:

python mains/roberta.py -c configs/roberta.json

For this run, the option use_special_tokens should be set to false. You can then execute:

python mains/bertweet.py -c configs/bertweet.json

Set the option use_special_tokens to true again, then do:

python mains/bertweet.py -c configs/bertweet.json

All runs were repeated with random_seed in [0, 1, 2, 3, 4].

Additional Data

Download and extract the folder from http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip. Run the following preprocessing script:

python data_processing/preprocess_additional_data.py -i path-to-downloaded-folder/training.1600000.processed.noemoticon.csv -o output_folder

To run a model, use the same command as described above and set the config options use_additional_data to true. Also set additional_negative_tweets_path and additional_negative_tweets_path to the respective files generated in the output folder from the preprocessing script.

Ensemble Learning

Simple Model Averaging

We used the 5 runs from the BERTweet section and gathered all class output probabilities (logged during prediction of the test set). Place the runs to ensemble (we sequentially averaged rs0 then rs0+rs1, then rs0+rs1+rs2, ...) inside a directory "input_directory". Run

python ensemble/ensemble_probabilities.py -i input_directory -o output_directory

Inside "output_directory" a file "ensemble_predictions.csv" will be generated.

Bagging

For bagging, one needs to train multiple models with the option "do_bootstrap_sampling" set to true. Then proceed as described in the simple model averaging section.

On Data Quality and Model Robustness

Repeat the following procedure for BERT and BERTweet:

  1. Train the model as described in the corresponding model's section.

  2. Train the model on tweets with the unmatched parentheses relevant to the Parentheses Rule removed. This is done by setting the config option remove_rule_patterns to true while keeping all other options the same.

Each model will save the validation data and labels in the model checkpoint directory (see beginning of Reproduce Results section)

  1. Evaluate the saved model from step 1 first on the validation data saved in step 1 and then on the validation data saved in step 2.

  2. Evaluate the saved model from step 2 first on the validation data saved in step 1 and then on the validation data saved in step 2.

This will leave you with 4 prediction files. For each of these, perform step 5:

  1. This will output confusion matrices and accuracies for both the Parentheses Rule and the given validation predictions.
rules/main.py -d "path_to_saved_valiation_tweets_saved_in_step_1" -l "path_to_saved_valiation_labels_saved_in_step_1" -p "path_to_saved_valiation_predictions"

Resource Requirements

All experiments of BERT, Roberta and BERTweet were run on ETH's Leonhard cluster using an Nvidia GeForce RTX 2080 Ti GPU. The runtimes per model were about 16 hours (26 hours with additional data) with 2 CPU cores and 64 GBs of memory for BERT and BERTweet and 96 GBs of memory for RoBERTa.

Acknowledgements

We would like to give credit to the following tools, libraries and frameworks that helped us during this project: