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Topics and Trends in Cognitive Science

This project aims to uncover the trends and topics of the annual Cognitive Science Society conference using dynamic topic modeling.

Results

Paper: Topics and Trends in Cognitive Science (2000-2017) - to be published in the Proceedings of the 40th Annual Conference of the Cognitive Science Society.

Website: Find similar papers!

Workflow

1. Obtaining CogSci papers

Download PDFs from Archives of the Cognitive Science Society Conference Proceedings

Copy PDFs into text_data_new/

2. Preprocessing

Process PDFs:

generate_dtm_input.py
- input:  PDFs in text_data_new/volume_{}/
- output: dtm_input_data/dtm_input-mult.dat
          dtm_input_data/dtm_input-seq.dat

3. Modeling

Use our DTM version: alexanderrich/dtm

Talk to your local High Performance Cluster correspondent how to set everything up.

Run the script run_all.s

4. Postprocessing

@Alex: How did you create dtm_processed_output.p?

Exporting model output into csv tables:

pickle_to_csv.ipynb
- input:  output/dtm_processed_output.p
- output: output/csv/year_doc_topic.csv
          output/csv/topicnames.csv
          output/csv/year_topic_word.csv

Exporting original data into csv tables:

doc_word_freq.ipynb
- input:  dtm_input_data/dtm_input-mult.dat
- output: output/csv/doc_word_freq.csv

5. Analysis & Figures

See R scripts in the folder R. The R scripts save all figures into the folder figures.

Due to the exploratory nature of this project there are several scripts and figures that did not make their way into the paper.

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Dynamic Topic Model for Cognitive Science

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