Incorporating Emotions into Health Mention Classification Task on Social Media
The code for the paper Incorporating Emotions into Health Mention Classification Task on Social Media.
Dataset
Our proposed approach was evaluated on the list of datasets below. All dataset need to be downloaded from the respective providers.
FLU2013
- Separating Fact from Fear: Tracking Flu Infections on Twitter. For more information, see http://michaeljpaul.com/downloads/flu_data.phpPHM2017
- Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media. For more information, see https://github.com/emory-irlab/PHM2017HMC2019
- Leveraging Sentiment Distributions to Distinguish Figurative From Literal Health Reports on Twitter. For more information, see https://github.com/biddle-r/HMC2019SELF2020
- Identifying Medical Self-Disclosure in Online Communities - For more information, contact, mvaliz2@uic.eduRHMD2022
- Identification of Disease or Symptom terms in Reddit to Improve Health Mention Classification - For more information, see https://github.com/usmaann/RHMD-Health-Mention-Dataset
Basic Usage
Preprocessing
python preprocess.py --data_path [path_to_data] \
--text_column [name_of_text_column] \
--label_column [name_of_label_column] \
--output_dir [path_to_save_processed_data]
HMC experiments
For intermediate task fine-tuning approach
python run_phm.py --train_file [train_file] \
--validation_file [validation_file] \
--test_file [test_file]\
--bert_model [path_to_emotion_model] \
--num_train_epochs [num_of_epochs] \
--per_device_train_batch_size [train_batch_size] \
--per_device_eval_batch_size [eval_batch_size] \
--model_type base
For multi-feature fusion approach
python run_phm.py --train_file [train_file] \
--validation_file [validation_file] \
--test_file [test_file]\
--bert_model bert-base-uncased \
--emotion_model [path_to_emotion_model] \
--num_train_epochs [num_of_epochs] \
--per_device_train_batch_size [train_batch_size] \
--per_device_eval_batch_size [eval_batch_size] \
--model_type multi_feature
Run finetune experiments
python run_finetune.py --train_file [train_file] \
--validation_file [validation_file] \
--bert_model bert-base-uncased \
--num_train_epochs [num_of_epochs] \
--per_device_train_batch_size [train_batch_size] \
--per_device_eval_batch_size [eval_batch_size] \
--output_dir [path_to_save_model] \
--model_type base
Requirements
python==3.9.6
torch==1.11.0
transformers==4.21.1