https://github.com/glambard/newsfeed_sanity

A simple and sane newsfeed for m.phys.org - or how to quickly recommend the best topics
https://github.com/glambard/newsfeed_sanity

extract gensim helper news-feed newsfeed sanity word2vec

Last synced: about 2 months ago
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A simple and sane newsfeed for m.phys.org - or how to quickly recommend the best topics

• Host: GitHub
• URL: https://github.com/glambard/newsfeed_sanity
• Owner: GLambard
• License: gpl-3.0
• Created: 2018-05-22T05:12:17.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-05-22T06:17:31.000Z (over 6 years ago)
• Last Synced: 2023-10-20T08:47:47.708Z (about 1 year ago)
• Topics: extract, gensim, helper, news-feed, newsfeed, sanity, word2vec
• Language: Jupyter Notebook
• Size: 26.4 KB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Newsfeed_Sanity

A simple and sane newsfeed for m.phys.org - or how to quickly recommend the best topics

## Requirements

* pandas

* numpy

* bs4

* cython

* gensim

* scipy

* webbrowser

* jupyterlab

* https://code.google.com/archive/p/word2vec/ (download: GoogleNews-vectors-negative300.bin)

## Goal

During the last few years, I was spending much time to read once per week the newsfeed from http://m.phys.org. That's a good exercise for keeping touch with the latest advancements in Science. But with the drawback that it's taking time, a lot of time, and mainly because I had to go through a long list of different topics that I didn't need. Also, I needed a newsfeeder (recommander) which respects my tastes without having to go through undesired proposals.

## Solution

Compress my reading habits using word2vec from gensim in order to automatically extract the relevant science subjects to me. Also, as I was manually scrapping the news through the http://m.phys.org website, I was of course tempted to open this or this article according to its title. This is fairly simple I know, but this is generally what we do, right? So let's start. 

## The notebook

The notebook **newsfeed_sanity.ipynb** above performs the following tasks: 

* Check your browsing history from Google Chrome for the last three months

* Extract the browsed webpages from http://m.phys.org

* Use gensim to vectorize the articles' topic with word2vec

* Compute the score = (1-distance) (higher is better here) between past and new topics 

* Rank the new topics by score

* Display the new topics as hyperlinks in a html page.

# Feel free to fork it, copy it, change it and improve it for your tastes and targeted websites. 

## This was an individual excercice, not a tutorial.