topic modeling on unstructured data in Space news articles retrieved from the Guardian (UK) newspaper using API

Overview

NLP Space News Topic Modeling

Photos by nasa.gov (1, 2, 3, 4, 5) and extremetech.com

Binder Open In Colab nbviewer pre-commit CI CodeQL License: MIT OpenSource Code style: black prs-welcome pyup

Table of Contents

  1. Project Idea
  2. Data acquisition
  3. Analysis
  4. Usage
  5. Project Organization

Project Idea

This project aims to learn topics published in Space news from the Guardian (UK) news publication1.

1: articles were also retrieved from the blog Space.com (web scraping), the New York Times (space news from the science section) and from the Hubble Telescope news archive, but these data sources were not used in analysis

Data acquisition

Primary data source

News articles are retrieved using the official API provided by the Guardian.

Supplementary data sources

Data is also acquired from articles published by the Hubble Telescope, the New York Times (US) and blog publication Space.com

Although these articles were acquired, they were not used in analysis.

Data file creation

  1. Use 1_get_list_of_urls.ipynb
    • programmatically retrieves urls from API or archive of publication
    • retrieves metadata such as date and time, section, sub-section, headline/abstract/short summary, etc.
  2. Use 2_scrape_urls.ipynb
    • scrapes news article text from publication url
  3. Use 3_merge_scraped_and_filter.ipynb
    • merge metadata (1_get_list_of_urls.ipynb) with scraped article text (2_scrape_urls.ipynb)

Analysis

Analysis will be performed using an un-supervised learning model. Details are included in the 8_gensim_coherence_nlp_trials_v3.ipynb notebook in the root directory.

Usage

  1. Clone this repository
    $ git clone
  2. Create Python virtual environment, install packages and launch interactive Python platform
    $ make build
  3. Run notebooks in the following order
    • 3_merge_scraped_and_filter.ipynb (view) (covers data from the Hubble news feed, New York Times and Space.com)
      • merge multiple files of articles text data retrieved from news publications API or archive
      • filter out articles of less than 500 words
      • export to *.csv file for use in unsupervised machine learning models
    • 8_gensim_coherence_nlp_trials_v3.ipynb (view) (does not cover data from the Hubble news feed, New York Times and Space.com)
      • experiments in selecting number of topics using
        • coherence score from built-in coherence model to score Gensim's NMF
        • sklearn's implementation of TFIDF + NMF, using best number of topics found using Gensim's NMF
      • manually reading articles that NMF associates with each topic
    • 9_nlp_workflow.ipynb (view)
      • code-only version of 9_gensim_coherence_nlp_trials_v3.ipynb, with necessary considerations for deployment of topic model

Project Organization

├── .pre-commit-config.yaml       <- configuration file for pre-commit hooks
├── .github
│   ├── workflows
│       └── integrate.yml         <- configuration file for Github Actions
├── LICENSE
├── environment.yml               <- configuration file to create environment to run project on Binder
├── Makefile                      <- Makefile with commands like `make lint` or `make build`
├── README.md                     <- The top-level README for developers using this project.
├── app
│   ├── data                      <- data exported from training topic modeler, for use with API
|   └── tests                     <- Source code for use in API tests
|       ├── test-logs             <- Reports from running unit tests on API
|       └── testing_utils         <- Source code for use in unit tests
|           └── *.py              <- Scripts to use in testing API routes
|       ├── __init__.py           <- Allows Python modules to be imported from testing_utils
|       └── test_api.py           <- Unit tests for API
├── api.py                        <- Defines API routes
├── pytest.ini                    <- Test configuration
├── requirements.txt              <- Packages required to run and test API
├── s*,t*.py                      <- Scripts to use in defining API routes
├── data
│   ├── raw                       <- raw data retrieved from news publication
|   └── processed                 <- merged and filtered data
├── executed-notebooks            <- Notebooks with output.
├── *.ipynb                       <- Jupyter notebooks. Naming convention is a number (for ordering),
│                                    and a short `-` delimited description
├── requirements.txt              <- packages required to execute all Jupyter notebooks interactively (not from CI)
├── setup.py                      <- makes project pip installable (pip install -e .) so `src` can be imported
├── src                           <- Source code for use in this project.
│   ├── __init__.py               <- Makes src a Python module
│   └── *.py                      <- Scripts to use in analysis for pre-processing, training, etc.
├── papermill_runner.py           <- Python functions that execute system shell commands.
└── tox.ini                       <- tox file with settings for running tox; see tox.testrun.org

Project based on the cookiecutter data science project template. #cookiecutterdatascience

Owner
edesz
edesz
📝An easy-to-use package to restore punctuation of the text.

✏️ rpunct - Restore Punctuation This repo contains code for Punctuation restoration. This package is intended for direct use as a punctuation restorat

Daulet Nurmanbetov 72 Dec 30, 2022
SIGIR'22 paper: Axiomatically Regularized Pre-training for Ad hoc Search

Introduction This codebase contains source-code of the Python-based implementation (ARES) of our SIGIR 2022 paper. Chen, Jia, et al. "Axiomatically Re

Jia Chen 17 Nov 09, 2022
Code to reproduce the results of the paper 'Towards Realistic Few-Shot Relation Extraction' (EMNLP 2021)

Realistic Few-Shot Relation Extraction This repository contains code to reproduce the results in the paper "Towards Realistic Few-Shot Relation Extrac

Bloomberg 8 Nov 09, 2022
中文医疗信息处理基准CBLUE: A Chinese Biomedical LanguageUnderstanding Evaluation Benchmark

English | 中文说明 CBLUE AI (Artificial Intelligence) is playing an indispensabe role in the biomedical field, helping improve medical technology. For fur

452 Dec 30, 2022
A program that uses real statistics to choose the best times to bet on BloxFlip's crash gamemode

Bloxflip Smart Bet A program that uses real statistics to choose the best times to bet on BloxFlip's crash gamemode. https://bloxflip.com/crash. THIS

43 Jan 05, 2023
Test finetuning of XLSR (multilingual wav2vec 2.0) for other speech classification tasks

wav2vec_finetune Test finetuning of XLSR (multilingual wav2vec 2.0) for other speech classification tasks Initial test: gender recognition on this dat

8 Aug 11, 2022
code for modular summarization work published in ACL2021 by Krishna et al

This repository contains the code for running modular summarization pipelines as described in the publication Krishna K, Khosla K, Bigham J, Lipton ZC

Approximately Correct Machine Intelligence (ACMI) Lab 21 Nov 24, 2022
NLP and Text Generation Experiments in TensorFlow 2.x / 1.x

Code has been run on Google Colab, thanks Google for providing computational resources Contents Natural Language Processing(自然语言处理) Text Classificati

1.5k Nov 14, 2022
Applied Natural Language Processing in the Enterprise - An O'Reilly Media Publication

Applied Natural Language Processing in the Enterprise This is the companion repo for Applied Natural Language Processing in the Enterprise, an O'Reill

Applied Natural Language Processing in the Enterprise 95 Jan 05, 2023
Deep learning for NLP crash course at ABBYY.

Deep NLP Course at ABBYY Deep learning for NLP crash course at ABBYY. Suggested textbook: Neural Network Methods in Natural Language Processing by Yoa

Dan Anastasyev 597 Dec 18, 2022
Labelling platform for text using distant supervision

With DataQA, you can label unstructured text documents using rule-based distant supervision.

245 Aug 05, 2022
Segmenter - Transformer for Semantic Segmentation

Segmenter - Transformer for Semantic Segmentation

592 Dec 27, 2022
precise iris segmentation

PI-DECODER Introduction PI-DECODER, a decoder structure designed for Precise Iris Segmentation and Location. The decoder structure is shown below: Ple

8 Aug 08, 2022
Entity Disambiguation as text extraction (ACL 2022)

ExtEnD: Extractive Entity Disambiguation This repository contains the code of ExtEnD: Extractive Entity Disambiguation, a novel approach to Entity Dis

Sapienza NLP group 121 Jan 03, 2023
Code for Text Prior Guided Scene Text Image Super-Resolution

Code for Text Prior Guided Scene Text Image Super-Resolution

82 Dec 26, 2022
Finally decent dictionaries based on Wiktionary for your beloved eBook reader.

eBook Reader Dictionaries Finally, decent dictionaries based on Wiktionary for your beloved eBook reader. Dictionaries Catalan 🚧 Ελληνικά (help welco

Mickaël Schoentgen 163 Dec 31, 2022
🎐 a python library for doing approximate and phonetic matching of strings.

jellyfish Jellyfish is a python library for doing approximate and phonetic matching of strings. Written by James Turk James Turk 1.8k Dec 21, 2022

Must-read papers on improving efficiency for pre-trained language models.

Must-read papers on improving efficiency for pre-trained language models.

Tobias Lee 89 Jan 03, 2023
DeBERTa: Decoding-enhanced BERT with Disentangled Attention

DeBERTa: Decoding-enhanced BERT with Disentangled Attention This repository is the official implementation of DeBERTa: Decoding-enhanced BERT with Dis

Microsoft 1.2k Jan 03, 2023