1. AVA_Comments_Full.txt : File contains the raw AVA_Comments with image IDs.
2. CLEAN_AVA_FULL_COMMENTS.json : A basic cleaning applied to the raw text data and saved as json
3. CLEAN_AVA_FULL_AFTER_SUBJECTIVE_CLEANING.json : AVA-Captions dataset with clean comments
4. AVA_TRAINING_FULL_DELIMITED_LDA_LABELLED.json: AVA-Captions json with LDA labels added for CNN training
5. LDA_AVA_200_50_passes_10000_iter_.p: LDA model trained on AVA-Captions dataset
6. _temp_topics_iteration_200.txt: Topic list corresponding to the model
7. AVA_FULL_VALIDATION.json: AVA-Captions validation set of 9362 images and corresponding captions. Format same as coco validation set.

1. clean_comment_json.py : Performs a basic cleaning of the text file #1 and returns #2
2. clean_using_subjectivity.py: Implements the probabilistic caption filtering strategy on #2 and returns #3
3. lda_with_n_grams.py: Runs LDA with 200 topics on #3 and returns #5 and #6
4. topic_modelling_labelling.py: Labels #3 and returns #4
5. spice_best.py: Computes spice for all reference captions and returns the best. This isn't implemented in COCO evaluation code and should be added inside coco-caption/pycocoevalcap/spice/

@article{ghosal2019aesthetic,
  title={Aesthetic Image Captioning From Weakly-Labelled Photographs},
  author={Ghosal, Koustav and Rana, Aakanksha and Smolic, Aljosa},
  journal={arXiv preprint arXiv:1908.11310},
  year={2019}
}