pip install -r requirements.txt

python scrape.py -s True -q [queries] -l [limit on tweets]  
python scrape.py -H True -f [files containing tweets ids]

Note : The -H stands for hydration, and -s for scraping. Restrictions related to coordinates, time intervals, can be modified directly in the script.

python train.py --exp_name (value) --encoder (value) --data_dir (value) --save_dir (value) --lr (value) --batch_size (value) --save_policy (value) --activation (value) --optim (value) --wd (value) --epochs (value) --seed (value) --use_gpu(to use gpu) --use_empath(to use empath) --l2(to use l2 reg.) --use_scheduler(to use sched) --use_dropout(to use dropout)

python generate_predictions.py --gpu_id (gpu_id) --model_name (BERT/ROBERTA) --model_path (path to saved model) --output_path (path to save dir) --data (path to dir containing hydrated csv) --use_empath (y/n) --activation (tanh/bce)

python plot.py -f (csv_or_tsv_file_with_predictions) -i(unique_id_field) -d(date_field) -e(field_with_emotion_predictions) -b(text_field) -l(boolean_flag_for_leap_year) -t(timestep_for_type_2) -c(chunk_size_for_type_1) -a(address_of_aspect_file)
Note-1: Plot types: (1)Fixed number of tweets, (2)Fixed time interval, (3)Aspect mentions (for fixed number of tweets out of the total)  
Note-2: By default the aspect term searching is case sensitive
Note-3: Aspects labels can be supplied in the code