Crawl and Visualize ICLR 2022 OpenReview Data

Descriptions

This Jupyter Notebook contains the data crawled from ICLR 2022 OpenReview webpages and their visualizations. The list of submissions (sorted by the average ratings) can be found here.

Prerequisites

• python 3.7
• Install requirements with pip install -r requirements.txt
  • NOTE: You can also use edgewebdriver on Windows environments by changing the path variable in the .py scripts

Crawl Data

1. Run crawl_paperlist.py to crawl the list of papers (~0.5h).
2. Run crawl_reviews.py to crawl the reviews (~1.5h).
   • NOTE: currently only review ratings are crawled.

Visualization

Keywords Frequency

The top 50 common keywords (uncased) and their frequency:

Keywords Cloud

The word clouds formed by keywords of submissions show the hot topics including deep learning, reinforcement learning, representation learning, graph neural network, etc.

Ratings Distribution

The distribution of reviewer ratings centers around 5 (mean: 4.942).

Keywords vs Ratings

The average reviewer ratings and the frequency of keywords indicate that to maximize your chance to get higher ratings would be using the keywords such as deep generative models, or normalizing flows.

All ICLR 2022 Submissions

Number of submissions: 3262 (Collected at 10/11/2021 09:42 PM UTC+9).