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.

1. FLU2013 - Separating Fact from Fear: Tracking Flu Infections on Twitter. For more information, see http://michaeljpaul.com/downloads/flu_data.php
2. PHM2017 - 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/PHM2017
3. HMC2019 - Leveraging Sentiment Distributions to Distinguish Figurative From Literal Health Reports on Twitter. For more information, see https://github.com/biddle-r/HMC2019
4. SELF2020 - Identifying Medical Self-Disclosure in Online Communities - For more information, contact, mvaliz2@uic.edu
5. RHMD2022 - 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