# Building a Zite Replacement (Part 2)

Posted by Graham Wheeler on Saturday, September 19, 2015

In the previous post I gave an overview of what needs to be built for our Zite replacement. In this post we will look at how to load an RSS feed and generate key terms for each article. In order to fetch the feed we will make use of the feedparser package, so make sure to install that first with pip, conda, or whatever you use.

Another thing we’re going to want is to strip HTML tags from the articles. I did a Google for “HTML element stripper Python” and found this StackOverflow post with the code below that works great:

#!python
from HTMLParser import HTMLParser

class MLStripper(HTMLParser):
def __init__(self):
self.reset()
self.fed = []
def handle_data(self, d):
self.fed.append(d)
def get_data(self):
return ''.join(self.fed)

def strip_tags(html):
s = MLStripper()
s.feed(html)
return s.get_data()


With that, let’s begin. One of the things we will want to do is normalize capitalization of words. In particular, the first word in a sentence is capitalized and we want to in most cases undo that so we don’t see two forms of the same word. One solution is to lower-case everything but that is throwing the baby out with the bathwater. A better solution is to look up each word in a dictionary. If we fail to find a match we can try the lower-case form.

There are probably libraries already to do this (NLTK?) but I did some early prototyping of the code in this post using the top 5000 words from this list. So for now I’ll just use that as a dictionary to normalize words. There are much more comprehensive lists out there without frequency information and I expect I’ll replace this but due to my early experiments I had the word list loaded already.

If you’re interested in using that list, you can copy/paste it into a spreadsheet and save it as a CSV once you have agreed to the terms. I used the code below to load that CSV file. I also put the top 198 words in a separate list that I was going to use to remove stop words, but I ended up not needing that.

#!python
import csv

words5000 = {}
total = 0
count = 0
top_words = []
with open('freq5000.csv', 'rb') as f:
total = 0
if row[3] == '' or row[3][0] > '9':
continue
v = int(row[3])
word = row[1].encode('ascii', 'ignore')
count += 1
if count <= 198:
top_words.append(word)
words5000[word] = v
total += v


Next we will use a function that gets a dictionary with the words and counts from an article, and returns that plus the total number of words in the dictionary. It will do all the necessary work to strip tags, punctuation, and so on before computing the counts.

#!python
import re
import string

def get_article_terms(article):
"""
Get the terms and counts from an article. Strip HTML tags and
non-essential punctuation, whitespace, and single
character 'words' like s that may come from punctuation removal.
Try normalize initial letter case using the 5000 word dictionary.
Return a set of words and counts and a total count.
"""
terms = {}
total = 0
# Remove the HTML element tags
article = strip_tags(article)
# replace non-ASCII chars with space
article = re.sub(r'[^A-Za-z\-]+',' ', article)
# We could use a Counter here but the would need to post-process the keys
# to remove the bogus ones and correct capitalization. Consider for later.
for term in article.split(' '):
if len(term) < 2 and (term != 'a' or term == 'A' or term == 'I'):
continue
total += 1
# Update the count. If the word is a new one then see if the lower
# case form is in our dictionary and use that in preference.
if term in terms:
terms[term] += 1
else:
lterm = term.lower()
if lterm in words5000:
term = lterm
terms[term] = 1

return terms, total


The method we will use to get topics is something called Term Frequency-Inverse Document Frequency (TF-IDF). It essentially works by taking the frequency of a term in a document, and multiplying it by the log of the inverse of the number of documents the term occurs in. The idea is that the key terms will be words that occur frequently in this document relative to how frequently they occur in all the documents. Obviously words like ’the’, ‘and’, etc have a high frequency in most documents, so they aren’t interesting. But if a word that is uncommon in all the documents, like, say, ‘pneumonia’, occur relatively higher in a specific document, then that word is likely important.

A key part of this is that we need to count how many documents a word occurs in, not just how often it occurs in each document. So our next function is going to look at all the articles in a feed. Ideally we would compute the IDFs from a bigger corpus of documents, so that can be refined later (in fact, I used the top 5000 words as an alternative and they worked quite well too, but if every article in a feed include some boiler plate text, using the top 5000 words may not filter that out, but computing IDF just from the articles will).

#!python
import collections
import feedparser

def get_article_data(feed_url):
"""
Calculate the term counts for each article in a feed as well as
the document counts for each term. Return a list of article metadata
including the title, a snippet, the distinct terms and their counts, etc,
as well as the counts of how many documents each term appeared in.
"""

d = feedparser.parse(feed_url)

doc_terms = collections.Counter()
articles = []

entries = d['entries']
for entry in entries:
title = entry.get('title', None)

# For some sites seem we get summary and some content
summary = entry.get('summary', None)
content = entry.get('content', None)
article = summary if content is None else content

terms, count = get_article_terms(article)
doc_terms.update(terms.keys())
'terms': terms, 'count': count,
'date': entry.get('published_parsed', None)})
return articles, doc_terms


Now we can use this function to compute the TF-IDFs for all the articles in a feed:

#!python
import math

def get_feed_with_tf_idf(feed_url):
""" Calculate TF-IDFs for each article in a feed and add to metadata """
articles, doc_terms = get_article_data(feed_url)
for article in articles:
terms = article['terms']
tf_idf = {}
article_count = float(article['count'])
for term, count in terms.items():
tf_idf[term] = (count / article_count) * math.log(len(articles) / float(doc_terms[term]))
article['tf_idf'] = tf_idf
return articles


And we’re basically done! Let’s see how well it worked. We can try it out on the HuffPost and print out the top ten terms for each article:

#!python
import operator

articles = get_feed_with_tf_idf('http://www.huffingtonpost.com/feeds/index.xml')
for article in articles:
rank = sorted(article['tf_idf'].items(), key=operator.itemgetter(1), reverse=True)
'\n'.join(x[0] for x in rank[:10]))


Running this right now gives the results below. Not bad for a first attempt! Note that the titles that are printed out were not included in the term counts and so are a good test (although in practice it would make sense to include these so they can help rank the terms).

iPhone Shopping? Beware: Madness Ahead!
If you're thinking of upgrading your aging iPhone - I've just gone through all the pain and sufferin
iPhone
ATT
Verizon
phone
customers
line
phones
sell
you
month

Ted Cruz Declines To Say Whether He Thinks Obama Is A Christian
<div class="embed-asset embed"><br />
<div class="embed-code"><span class="js-fivemin-script
Cruz
Christian
president
policies
speculate
Muslim
contributed
Obama
faith

How to Help Shatter the Class Ceiling - Elect Bernie Sanders
Let's make history. The 2016 election offers a rare moment to crack a barrier that can truly transfo
percent
Sanders
poverty
income
Sen
far
African-Americans
wealth
trillion
households

2-Year-Old Baby, 2 Others Gunned Down Inside Utah Home
<br />
<p class="ap-story-p">SALT LAKE CITY (AP) -- Police in Utah arrested a 32-year-old man on Sat
Chipping
Tran
inside
boy
-year-old
home
victims
arrested
Poike
bodies

Pope Francis Praises U.S.-Cuba Detente As Model For World
<br />
<p>HAVANA (AP) &mdash; Pope Francis hailed detente between the United States and Cuba as a mo
Cuba
Francis
visit
Cuban
pope
Raul
United
reconciliation
airport
travel

Reminding Yourself You're F&^*% Hot (Step 1)
Ladies, there are so many facets in our struggle of masculine dominated life that project us to eith
Kyle
hot
hotness
itself
swear
moment
was
re
know
exact

A Pope That Congress Should Listen To
When the Pope takes center stage in the heart of the nation's capital next Thursday, Americans will
Pope
values
village
community
energy
Iran
person
culture
peace
world

Donald Trump & Vaccines: Is He Ready To Be Responsible For A Children's Epidemic?
<strong>Donald Trump may be a big blowhard, espousing his belief that there's a link between vaccine
vaccines
Donald
Trump
children
doses
effective
vaccinations
diseases
Carson
debate

What You Should Do If You Own A Volkswagen That Was Just Recalled
<div class="embed-asset embed"><br />
<div class="embed-code"><span class="js-fivemin-script
Volkswagen
vehicles
loaner
recalled
car
cars
risking
diesel-power
vehicle
safety

There are times throughout a leader's tenure that he/she must look behind them and see if anyone is
team
regularly
disorganized
clarity
praise
follow
you

How To Deal With These 3 Difficult Managers At Work
One of the biggest challenges for any employee is learning how to work well with a manager. A good w
manager
Gary
Pleasing
Patricia
personality
difficult
Mitchell
Micromanaging
Goal-Focused
she

Experts: Cybersecurity Attacks Slow Ahead Of Obama's Meeting With Chinese President
<br />
<p>WASHINGTON, Sept 19 (Reuters) - Major intrusions by Chinese hackers of U.S. companies' com
Chinese
cybersecurity
Mandia
China
said
breaches
companies
McClure
Inc
Xi

6 Tips for the Unmotivated Student: How to Stay on Track After the First Week of School
study
buddy
semester
your
home
media
studying
excited
coffee
finding

It's sad to think how many people go their whole lives dreaming of goals that they will never hit. W
goals
hit
everyday
everything
your
ll
want
habit
consistent

Huckabee: Obama Nominated Openly Gay Army Head To 'Appease Homosexuals'
<div class="embed-asset embed"><br />
<div class="embed-code"><span class="js-fivemin-script
military
Fanning
Huckabee
Carter
gay
nomination
openly
undersecretary
appointment
experiments