How to Extract Text from Website

Extracting data from the Web using scripts (web scraping) is widely used today for numerous purposes. One of the parts of this process is downloading actual text from urls. This will be the topic of this post.

We will consider how it can be done using the following case examples:
Extracting information from visited links of history of using Chrome browser.

Extracting information from list of links. For example in the previous post we looked how to extract links from twitter search results into csv file. This file will be now the source of links.

Below will follow the python script implementation of main parts. It is using few code snippets and posts from the web. References and full source code are provided in the end.

Switching Between Cases
The script is using a variable USE_LINKS_FROM_CHROME_HISTORY to select correct program flow. If USE_LINKS_FROM_CHROME_HISTORY is true it will start extract links from Chrome, otherwise will use file with links.

results=[]
if  USE_LINKS_FROM_CHROME_HISTORY:
        results =  get_links_from_chrome_history() 
        fname="data_from_chrome_history_links.csv"
else:
        results=get_links_from_csv_file()
        fname="data_from_file_links.csv"

Extracting Content From HTML Links
We use python libraries BeautifulSoup for processing HTML and requests library for downloading HTML:

from bs4 import BeautifulSoup
from bs4.element import Comment
import requests

def tag_visible(element):
    if element.parent.name in ['style', 'script', 'head',  'meta', '[document]']:
        return False
    if isinstance(element, Comment):
        return False
    return True

def get_text(url):
   print (url) 
   
   try:
      req  = requests.get(url, timeout=5)
   except: 
      return "TIMEOUT ERROR"  
  
   data = req.text
   soup = BeautifulSoup(data, "html.parser")
   texts = soup.findAll(text=True)
   visible_texts = filter(tag_visible, texts)  
   return u" ".join(t.strip() for t in visible_texts)

Extracting Content from PDF Format with PDF to Text Python

Not all links will give html page. Some might lead to pdf data format page. For this we need to use specific process of getting text from pdf. There are several solutions possible. Here we will use pdftotext exe file. [2] With this method we create function as below and call it when url ends with “.pdf”.

To make actual conversion from pdf to txt we use subprocess.call and provide location of pdftotext.exe file, filename of pdf file and filename of new txt file. Note that we first download pdf page to pdf file on local drive.

import subprocess
def get_txt_from_pdf(url):
    myfile = requests.get(url, timeout=8)
    myfile_name=url.split("/")[-1] 
    myfile_name_wout_ext=myfile_name[0:-4]
    open('C:\\Users\\username\\Downloads\\' + myfile_name, 'wb').write(myfile.content)
    subprocess.call(['C:\\Users\\username\\pythonrun\\pdftotext' + '\\pdftotext', myfile_name, myfile_name_wout_ext+".txt"])
    with open('C:\\Users\\username\\Downloads\\' + myfile_name_wout_ext+".txt", 'r') as content_file:
        content = content_file.read()
    return content  

 if url.endswith(".pdf"):
                  txt = get_txt_from_pdf(full_url)

Cleaning Extracted Text
Once text is extracted from pdf or html we need to remove not useful text.
Below are processing actions that are implemented in the script:

  • remove non content text like script, html, tags (it is only for html pages)
  • remove non text characters
  • remove repeating spaces
  • remove documents if the size of document less then some min number of characters (MIN_LENGTH_of_document)
  • remove bad requests results – for example the request to get content from specific link was not successful but still resulted in some text.

Getting Links from Chrome History
To get visited links we need query Chrome web browser database with simple SQL statement. This is well described on some other web blogs. You can find link also in the references below [1].

Additionally when we extracting from Chrome history we need remove links that are out of scope – example you are extracting links that you used for reading about data mining. So links where you access your banking site or friends on facebook are not related.

To sort out not related links we can insert in sql statement filtering criteria with NOT Like * or <> as below:
select_statement = “SELECT urls.url FROM urls WHERE urls.url NOT Like ‘%localhost%’ AND urls.url NOT Like ‘%google%’ AND urls.visit_count > 0 AND urls.url <> ‘https://www.reddit.com/’ ;”

Conclusion
We learned how to extract text from website (pdf or html). We built the script for two practical examples: when we use links from Chrome web browser history or when we have list of links extracted from somewhere, for example from Twitter search results. The next step would be extract insights from the obtained text data using machine learning or text mining. For example from chrome history we could build frequent questions that developer searches in the web browser and create faster way to access information.

# -*- coding: utf-8 -*-

import os
import sqlite3
import operator
from collections import OrderedDict

import time
import csv

from bs4 import BeautifulSoup
from bs4.element import Comment
import requests
import re
import subprocess


MIN_LENGTH_of_document = 40
MIN_LENGTH_of_word = 2
USE_LINKS_FROM_CHROME_HISTORY = False #if false will use from csv file

def remove_min_words(txt):
   
   shortword = re.compile(r'\W*\b\w{1,1}\b')
   return(shortword.sub('', txt))


def clean_txt(text):
   text = re.sub('[^A-Za-z.  ]', ' ', text)
   text=' '.join(text.split())
   text = remove_min_words(text)
   text=text.lower()
   text = text if  len(text) >= MIN_LENGTH_of_document else ""
   return text

def tag_visible(element):
    if element.parent.name in ['style', 'script', 'head',  'meta', '[document]']:
        return False
    if isinstance(element, Comment):
        return False
    return True


  
    
def get_txt_from_pdf(url):
    myfile = requests.get(url, timeout=8)
    myfile_name=url.split("/")[-1] 
    myfile_name_wout_ext=myfile_name[0:-4]
    open('C:\\Users\\username\\Downloads\\' + myfile_name, 'wb').write(myfile.content)
    subprocess.call(['C:\\Users\\username\\pythonrun\\pdftotext' + '\\pdftotext', myfile_name, myfile_name_wout_ext+".txt"])
    with open('C:\\Users\\username\\Downloads\\' + myfile_name_wout_ext+".txt", 'r') as content_file:
        content = content_file.read()
    return content    


def get_text(url):
   print (url) 
   
   try:
      req  = requests.get(url, timeout=5)
   except: 
      return "TIMEOUT ERROR"  
  
   data = req.text
   soup = BeautifulSoup(data, "html.parser")
   texts = soup.findAll(text=True)
   visible_texts = filter(tag_visible, texts)  
   return u" ".join(t.strip() for t in visible_texts)


def parse(url):
	try:
		parsed_url_components = url.split('//')
		sublevel_split = parsed_url_components[1].split('/', 1)
		domain = sublevel_split[0].replace("www.", "")
		return domain
	except IndexError:
		print ("URL format error!")


def get_links_from_chrome_history():
   #path to user's history database (Chrome)
   data_path = os.path.expanduser('~')+"\\AppData\\Local\\Google\\Chrome\\User Data\\Default"
 
   history_db = os.path.join(data_path, 'history')

   #querying the db
   c = sqlite3.connect(history_db)
   cursor = c.cursor()
   select_statement = "SELECT urls.url FROM urls WHERE urls.url NOT Like '%localhost%' AND urls.url NOT Like '%google%' AND urls.visit_count > 0 AND urls.url <> 'https://www.reddit.com/' ;"
   cursor.execute(select_statement)

   results_tuples = cursor.fetchall() 
  
   return ([x[0] for x in results_tuples])
   
   
def get_links_from_csv_file():
   links_from_csv = []
   
   filename = 'C:\\Users\\username\\pythonrun\\links.csv'
   col_id=0
   with open(filename, newline='', encoding='utf-8-sig') as f:
      reader = csv.reader(f)
     
      try:
        for row in reader:
            
            links_from_csv.append(row[col_id])
      except csv.Error as e:
        print('file {}, line {}: {}'.format(filename, reader.line_num, e))
   return links_from_csv   
   
 
results=[]
if  USE_LINKS_FROM_CHROME_HISTORY:
        results =  get_links_from_chrome_history() 
        fname="data_from_chrome_history_links.csv"
else:
        results=get_links_from_csv_file()
        fname="data_from_file_links.csv"
        
        

sites_count = {} 
full_sites_count = {}



with open(fname, 'w', encoding="utf8", newline='' ) as csvfile: 
  fieldnames = ['URL', 'URL Base', 'TXT']
  writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
  writer.writeheader()

  
  count_url=0
  for url in results:    
      print (url)
      full_url=url
      url = parse(url)
      
      if full_url in full_sites_count:
            full_sites_count[full_url] += 1
      else:
            full_sites_count[full_url] = 1
          
            if url.endswith(".pdf"):
                  txt = get_txt_from_pdf(full_url)
            else:
                  txt = get_text(full_url)
            txt=clean_txt(txt)
            writer.writerow({'URL': full_url, 'URL Base': url, 'TXT': txt})
            time.sleep(4)
      
      
      
     
      if url in sites_count:
            sites_count[url] += 1
      else:
            sites_count[url] = 1
   
      count_url +=1

References
1. Analyze Chrome’s Browsing History with Python
2. XpdfReader
3. Python: Remove words from a string of length between 1 and a given number
4. BeautifulSoup Grab Visible Webpage Text
5. Web Scraping 101 with Python & Beautiful Soup
6. Downloading Files Using Python (Simple Examples)
7. Introduction to web scraping in Python
8. Ultimate guide to deal with Text Data (using Python) – for Data Scientists and Engineers

Twitter Text Mining with Python

In this post (and few following posts) we will look how to get interesting information by extracting links from results of Twitter search by keywords and using machine learning text mining. While there many other posts on the same topic, we will cover also additional small steps that are needed to process data. This includes such tasks as unshorting urls, setting date interval, saving or reading information.

Below we will focus on extracting links from results of Twitter search API python.

Getting Login Information for Twitter API

The first step is set up application on Twitter and get login information. This is already described in some posts on the web [1].
Below is the code snippet for this:

your code here
import tweepy as tw
    
CONSUMER_KEY ="xxxxx"
CONSUMER_SECRET ="xxxxxxx"
OAUTH_TOKEN = "xxxxx"
OAUTH_TOKEN_SECRET = "xxxxxx"

auth = tw.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
auth.set_access_token(OAUTH_TOKEN, OAUTH_TOKEN_SECRET)
api = tw.API(auth, wait_on_rate_limit=True)

Defining the Search Values

Now you can do search by keywords or hashtags and get tweets.
When we do search we might want to specify the start day so it will give results dated on this start day or after.

For this we can code as the following:

from datetime import datetime
from datetime import timedelta

NUMBER_of_TWEETS = 20
SEARCH_BEHIND_DAYS=60
today_date=datetime.today().strftime('%Y-%m-%d')


today_date_datef = datetime.strptime(today_date, '%Y-%m-%d')
start_date = today_date_datef - timedelta(days=SEARCH_BEHIND_DAYS)


for search_term in search_terms:
  tweets = tw.Cursor(api.search,
                   q=search_term,
                   lang="en",
                   since=SEARCH_BEHIND_DAYS).items(NUMBER_of_TWEETS)

The above search will return 20 tweets and will look only within 60 days from day of search. If we want use fixed date we can replace with since=’2019-12-01′

Processing Extracted Links

Once we got tweets text we can extract links. However we will get different types of links. Some are internal twitter links, some are shorten, some are regular urls.

So here is the function to sort out the links. We do not need internal links – the links that belong to Twitter navigation or other functionality.

try:
    import urllib.request as urllib2
except ImportError:
    import urllib2


import http.client
import urllib.parse as urlparse   

def unshortenurl(url):
    parsed = urlparse.urlparse(url) 
    h = http.client.HTTPConnection(parsed.netloc) 
    h.request('HEAD', parsed.path) 
    response = h.getresponse() 
    if response.status >= 300 and response.status < 400 and response.getheader('Location'):
        return response.getheader('Location') 
    else: return url 

Once we got links we can save urls information in csv file. Together with the link we save twitter text, date.
Additionally we count number of hashtags and links and also save this information into csv files. So the output of program is 3 csv files.

Conclusion

Looking in the output file we can quickly identify the links of interest. For example just during the testing this script I found two interesting links that I was not aware. In the following post we will look how to do even more automation for finding cool links using Twitter text mining.

Below you can find full source code and the references to web resources that were used for this post or related to this topic.

# -*- coding: utf-8 -*-

import tweepy as tw
import re
import csv

from datetime import datetime
from datetime import timedelta

NUMBER_of_TWEETS = 20
SEARCH_BEHIND_DAYS=60
today_date=datetime.today().strftime('%Y-%m-%d')


today_date_datef = datetime.strptime(today_date, '%Y-%m-%d')
start_date = today_date_datef - timedelta(days=SEARCH_BEHIND_DAYS)
try:
    import urllib.request as urllib2
except ImportError:
    import urllib2


import http.client
import urllib.parse as urlparse   

def unshortenurl(url):
    parsed = urlparse.urlparse(url) 
    h = http.client.HTTPConnection(parsed.netloc) 
    h.request('HEAD', parsed.path) 
    response = h.getresponse() 
    if response.status >= 300 and response.status < 400 and response.getheader('Location'):
        return response.getheader('Location') 
    else: return url    
    
    
CONSUMER_KEY ="xxxxx"
CONSUMER_SECRET ="xxxxxxx"
OAUTH_TOKEN = "xxxxxxxx"
OAUTH_TOKEN_SECRET = "xxxxxxx"


auth = tw.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
auth.set_access_token(OAUTH_TOKEN, OAUTH_TOKEN_SECRET)
api = tw.API(auth, wait_on_rate_limit=True)
# Create a custom search term 

search_terms=["#chatbot -filter:retweets", 
              "#chatbot+machine_learning -filter:retweets", 
              "#chatbot+python -filter:retweets",
              "text classification -filter:retweets",
              "text classification python -filter:retweets",
              "machine learning applications -filter:retweets",
              "sentiment analysis python  -filter:retweets",
              "sentiment analysis  -filter:retweets"]
              
        
              
def count_urls():
       url_counted = dict() 
       url_count = dict()
       with open('data.csv', 'r', encoding="utf8" ) as csvfile: 
           line = csvfile.readline()
           while line != '':  # The EOF char is an empty string
            
               line = csvfile.readline()
               items=line.split(",")
               if len(items) < 3 :
                          continue
                           
               url=items[1]
               twt=items[2]
               # key =  Tweet and Url
               key=twt[:30] + "___" + url
               
               if key not in url_counted:
                      url_counted[key]=1
                      if url in url_count:
                           url_count[url] += 1
                      else:
                           url_count[url] = 1
       print_count_urls(url_count)             

       
def print_count_urls(url_count_data):
   
         for key, value in url_count_data.items():
              print (key, "=>", value)
              
         with open('data_url_count.csv', 'w', encoding="utf8", newline='' ) as csvfile_link_count: 
            fieldnames = ['URL', 'Count']
            writer = csv.DictWriter(csvfile_link_count, fieldnames=fieldnames)
            writer.writeheader() 
            
            for key, value in url_count_data.items():
                 writer.writerow({'URL': key, 'Count': value })   
            
           
def extract_hash_tags(s):
    return set(part[1:] for part in s.split() if part.startswith('#'))
    

   
def save_tweet_info(tw, twt_dict, htags_dict ):
   
    if tw not in twt_dict:
        htags=extract_hash_tags(tw)
        twt_dict[tw]=1
        for ht in htags:
            if ht in htags_dict:
                htags_dict[ht]=htags_dict[ht]+1
            else:   
                htags_dict[ht]=1


def print_count_hashtags(htags_count_data):
        
         for key, value in htags_count_data.items():
              print (key, "=>", value)
              
         with open('data_htags_count.csv', 'w', encoding="utf8", newline='' ) as csvfile_link_count: 
            fieldnames = ['Hashtag', 'Count']
            writer = csv.DictWriter(csvfile_link_count, fieldnames=fieldnames)
            writer.writeheader() 
            
            for key, value in htags_count_data.items():
                 writer.writerow({'Hashtag': key, 'Count': value })          
        


tweet_dict = dict() 
hashtags_dict = dict()

                 
for search_term in search_terms:
  tweets = tw.Cursor(api.search,
                   q=search_term,
                   lang="en",
                   #since='2019-12-01').items(40)
                   since=SEARCH_BEHIND_DAYS).items(NUMBER_of_TWEETS)

  with open('data.csv', 'a', encoding="utf8", newline='' ) as csvfile: 
     fieldnames = ['Search', 'URL', 'Tweet', 'Entered on']
     writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
     writer.writeheader()
     

     for tweet in tweets:
         urls = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', tweet.text)
   
         save_tweet_info(tweet.text, tweet_dict, hashtags_dict ) 
         for url in urls:
          try:
            res = urllib2.urlopen(url)
            actual_url = res.geturl()
         
            if ( ("https://twitter.com" in actual_url) == False):
                
                if len(actual_url) < 32:
                    actual_url =unshortenurl(actual_url) 
                print (actual_url)
              
                writer.writerow({'Search': search_term, 'URL': actual_url, 'Tweet': tweet.text, 'Entered on': today_date })
              
          except:
              print (url)    

            
print_count_hashtags(hashtags_dict)
count_urls()      

References

1. Text mining: Twitter extraction and stepwise guide to generate a word cloud
2. Analyze Word Frequency Counts Using Twitter Data and Tweepy in Python
3. unshorten-url-in-python-3
4. how-can-i-un-shorten-a-url-using-python
5. extracting-external-links-from-tweets-in-python

Document Similarity, Tokenization and Word Vectors in Python with spaCY

Calculating document similarity is very frequent task in Information Retrieval or Text Mining. Years ago we would need to build a document-term matrix or term-document matrix that describes the frequency of terms that occur in a collection of documents and then do word vectors math to find similarity. Now by using spaCY it can be done just within few lines. Below you will find how to get document similarity , tokenization and word vectors with spaCY.

spaCY is an open-source library designed to help you build NLP applications. It has a lot of features, we will look in this post only at few but very useful.

Document Similarity

Here is how to get document similarity:

import spacy
nlp = spacy.load('en')

doc1 = nlp(u'Hello this is document similarity calculation')
doc2 = nlp(u'Hello this is python similarity calculation')
doc3 = nlp(u'Hi there')

print (doc1.similarity(doc2)) 
print (doc2.similarity(doc3)) 
print (doc1.similarity(doc3))  

Output:
0.94
0.33
0.30

In more realistic situations we would load documents from files and would have longer text. Here is the experiment that I performed. I saved 3 articles from different random sites, two about deep learning and one about feature engineering.

def get_file_contents(filename):
  with open(filename, 'r') as filehandle:  
    filecontent = filehandle.read()
    return (filecontent) 

fn1="deep_learning1.txt"
fn2="feature_eng.txt"
fn3="deep_learning.txt"

fn1_doc=get_file_contents(fn1)
print (fn1_doc)

fn2_doc=get_file_contents(fn2)
print (fn2_doc)

fn3_doc=get_file_contents(fn3)
print (fn3_doc)
 
doc1 = nlp(fn1_doc)
doc2 = nlp(fn2_doc)
doc3 = nlp(fn3_doc)
 
print ("dl1 - features")
print (doc1.similarity(doc2)) 
print ("feature - dl")
print (doc2.similarity(doc3)) 
print ("dl1 - dl")
print (doc1.similarity(doc3)) 
 
"""
output:
dl1 - features
0.9700237040142454
feature - dl
0.9656364096761337
dl1 - dl
0.9547075478662724
"""


It was able to assign higher similarity score for documents with similar topics!

Tokenization

Another very useful and simple feature that can be done with spaCY is tokenization. Here is how easy to convert text into tokens (words):

for token in doc1:
    print(token.text)
    print (token.vector)

Word Vectors

spaCY has integrated word vectors support, while other libraries like NLTK do not have it. Below line will print word embeddings – array of 768 numbers on my environment.

 
print (token.vector)   #-  prints word vector form of token. 
print (doc1[0].vector) #- prints word vector form of first token of document.
print (doc1.vector)    #- prints mean vector form for doc1

So we looked how to use few features (similarity, tokenization and word embeddings) which are very easy to implement with spaCY. I hope you enjoyed this post. If you have any tips or anything else to add, please leave a comment below.

References
1. spaCY
2. Word Embeddings in Python with Spacy and Gensim

FastText Word Embeddings for Text Classification with MLP and Python

Word embeddings are widely used now in many text applications or natural language processing moddels. In the previous posts I showed examples how to use word embeddings from word2vec Google, glove models for different tasks including machine learning clustering:

GloVe – How to Convert Word to Vector with GloVe and Python

word2vec – Vector Representation of Text – Word Embeddings with word2vec

word2vec application – K Means Clustering Example with Word2Vec in Data Mining or Machine Learning

In this post we will look at fastText word embeddings in machine learning. You will learn how to load pretrained fastText, get text embeddings and do text classification. As stated on fastText site – text classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. [1]

What is fastText

fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. [1]

fastText, is created by Facebook’s AI Research (FAIR) lab. The model is an unsupervised learning algorithm for obtaining vector representations for words. Facebook makes available pretrained models for 294 languages.[2]

As per Quora [6], Fasttext treats each word as composed of character ngrams. So the vector for a word is made of the sum of this character n grams. Word2vec (and glove) treat words as the smallest unit to train on. This means that fastText can generate better word embeddings for rare words. Also fastText can generate word embeddings for out of vocabulary word but word2vec and glove can not do this.

Word Embeddings File

I downloaded wiki file wiki-news-300d-1M.vec from here [4], but there are some other links where you can download different data files. I found this one has smaller size so it is easy to work with it.

Basic Operations with fastText Word Embeddings

To get most similar words to some word:

from gensim.models import KeyedVectors
model = KeyedVectors.load_word2vec_format('wiki-news-300d-1M.vec')
print (model.most_similar('desk'))

"""
[('desks', 0.7923153638839722), ('Desk', 0.6869951486587524), ('desk.', 0.6602819561958313), ('desk-', 0.6187258958816528), ('credenza', 0.5955315828323364), ('roll-top', 0.5875717401504517), ('rolltop', 0.5837830305099487), ('bookshelf', 0.5758029222488403), ('Desks', 0.5755287408828735), ('sofa', 0.5617446899414062)]
"""

Load words in vocabulary:

words = []
for word in model.vocab:
    words.append(word)

To see embeddings:

print("Vector components of a word: {}".format(
    model[words[0]]
))

"""
Vector components of a word: [-0.0451  0.0052  0.0776 -0.028   0.0289  0.0449  0.0117 -0.0333  0.1055
 .......................................
 -0.1368 -0.0058 -0.0713]
"""

The Problem

So here we will use fastText word embeddings for text classification of sentences. For this classification we will use sklean Multi-layer Perceptron classifier (MLP).
The sentences are prepared and inserted into script:

sentences = [['this', 'is', 'the', 'good', 'machine', 'learning', 'book'],
			['this', 'is',  'another', 'machine', 'learning', 'book'],
			['one', 'more', 'new', 'book'],
		
          ['this', 'is', 'about', 'machine', 'learning', 'post'],
          ['orange', 'juice', 'is', 'the', 'liquid', 'extract', 'of', 'fruit'],
          ['orange', 'juice', 'comes', 'in', 'several', 'different', 'varieties'],
          ['this', 'is', 'the', 'last', 'machine', 'learning', 'book'],
          ['orange', 'juice', 'comes', 'in', 'several', 'different', 'packages'],
          ['orange', 'juice', 'is', 'liquid', 'extract', 'from', 'fruit', 'on', 'orange', 'tree']]

The sentences belong to two classes, the labels for classes will be assigned later as 0,1. So our problem is to classify above sentences. Below is the flowchart of the program that we will use for perceptron learning algorithm example.

Text classification using word embeddings
Text classification using word embeddings

Data Preparation

I converted this text input into digital using the following code. Basically I got word embedidings and averaged all words in the sentences. The resulting vector sentence representations were saved to array V.

import numpy as np

def sent_vectorizer(sent, model):
    sent_vec =[]
    numw = 0
    for w in sent:
        try:
            if numw == 0:
                sent_vec = model[w]
            else:
                sent_vec = np.add(sent_vec, model[w])
            numw+=1
        except:
            pass
   
    return np.asarray(sent_vec) / numw


V=[]
for sentence in sentences:
    V.append(sent_vectorizer(sentence, model))   

After converting text into vectors we can divide data into training and testing datasets and attach class labels.

X_train = V[0:6]
X_test = V[6:9] 
          
Y_train = [0, 0, 0, 0, 1,1]
Y_test =  [0,1,1]   

Text Classification

Now it is time to load data to MLP Classifier to do text classification.

from sklearn.neural_network import MLPClassifier
classifier = MLPClassifier(alpha = 0.7, max_iter=400) 
classifier.fit(X_train, Y_train)

df_results = pd.DataFrame(data=np.zeros(shape=(1,3)), columns = ['classifier', 'train_score', 'test_score'] )
train_score = classifier.score(X_train, Y_train)
test_score = classifier.score(X_test, Y_test)

print  (classifier.predict_proba(X_test))
print  (classifier.predict(X_test))

df_results.loc[1,'classifier'] = "MLP"
df_results.loc[1,'train_score'] = train_score
df_results.loc[1,'test_score'] = test_score

print(df_results)
     
"""
Output
  classifier  train_score  test_score
         MLP          1.0         1.0
"""

In this post we learned how to use pretrained fastText word embeddings for converting text data into vector model. We also looked how to load word embeddings into machine learning algorithm. And in the end of post we looked at machine learning text classification using MLP Classifier with our fastText word embeddings. You can find full python source code and references below.

from gensim.models import KeyedVectors
import pandas as pd

model = KeyedVectors.load_word2vec_format('wiki-news-300d-1M.vec')
print (model.most_similar('desk'))

words = []
for word in model.vocab:
    words.append(word)

print("Vector components of a word: {}".format(
    model[words[0]]
))
sentences = [['this', 'is', 'the', 'good', 'machine', 'learning', 'book'],
			['this', 'is',  'another', 'machine', 'learning', 'book'],
			['one', 'more', 'new', 'book'],
	    ['this', 'is', 'about', 'machine', 'learning', 'post'],
          ['orange', 'juice', 'is', 'the', 'liquid', 'extract', 'of', 'fruit'],
          ['orange', 'juice', 'comes', 'in', 'several', 'different', 'varieties'],
          ['this', 'is', 'the', 'last', 'machine', 'learning', 'book'],
          ['orange', 'juice', 'comes', 'in', 'several', 'different', 'packages'],
          ['orange', 'juice', 'is', 'liquid', 'extract', 'from', 'fruit', 'on', 'orange', 'tree']]
         
import numpy as np

def sent_vectorizer(sent, model):
    sent_vec =[]
    numw = 0
    for w in sent:
        try:
            if numw == 0:
                sent_vec = model[w]
            else:
                sent_vec = np.add(sent_vec, model[w])
            numw+=1
        except:
            pass
   
    return np.asarray(sent_vec) / numw

V=[]
for sentence in sentences:
    V.append(sent_vectorizer(sentence, model))   
         
    
X_train = V[0:6]
X_test = V[6:9] 
Y_train = [0, 0, 0, 0, 1,1]
Y_test =  [0,1,1]    
    
    
from sklearn.neural_network import MLPClassifier
classifier = MLPClassifier(alpha = 0.7, max_iter=400) 
classifier.fit(X_train, Y_train)

df_results = pd.DataFrame(data=np.zeros(shape=(1,3)), columns = ['classifier', 'train_score', 'test_score'] )
train_score = classifier.score(X_train, Y_train)
test_score = classifier.score(X_test, Y_test)

print  (classifier.predict_proba(X_test))
print  (classifier.predict(X_test))

df_results.loc[1,'classifier'] = "MLP"
df_results.loc[1,'train_score'] = train_score
df_results.loc[1,'test_score'] = test_score
print(df_results)

References
1. fasttext.cc
2. fastText
3.
Classification with scikit learn
4. english-vectors
5. How to use pre-trained word vectors from Facebook’s fastText
6. What is the main difference between word2vec and fastText?

How to Convert Word to Vector with GloVe and Python

In the previous post we looked at Vector Representation of Text with word embeddings using word2vec. Another approach that can be used to convert word to vector is to use GloVe – Global Vectors for Word Representation. Per documentation from home page of GloVe [1] “GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus”. Thus we can convert word to vector using GloVe.

At this post we will look how to use pretrained GloVe data file that can be downloaded from [1].
word embeddings GloVe We will look how to get word vector representation from this downloaded datafile. We will also look how to get nearest words. Why do we need vector representation of text? Because this is what we input to machine learning or data science algorithms – we feed numerical vectors to algorithms such as text classification, machine learning clustering or other text analytics algorithms.

Loading Glove Datafile

The code that I put here is based on some examples that I found on StackOverflow [2].

So first you need to open the file and load data into the model. Then you can get the vector representation and other things.

Below is the full source code for glove python script:

file = "C:\\Users\\glove\\glove.6B.50d.txt"
import numpy as np
def loadGloveModel(gloveFile):
    print ("Loading Glove Model")
   
    
    with open(gloveFile, encoding="utf8" ) as f:
       content = f.readlines()
    model = {}
    for line in content:
        splitLine = line.split()
        word = splitLine[0]
        embedding = np.array([float(val) for val in splitLine[1:]])
        model[word] = embedding
    print ("Done.",len(model)," words loaded!")
    return model
    
    
model= loadGloveModel(file)   

print (model['hello'])

"""
Below is the output of the above code
Loading Glove Model
Done. 400000  words loaded!
[-0.38497   0.80092   0.064106 -0.28355  -0.026759 -0.34532  -0.64253
 -0.11729  -0.33257   0.55243  -0.087813  0.9035    0.47102   0.56657
  0.6985   -0.35229  -0.86542   0.90573   0.03576  -0.071705 -0.12327
  0.54923   0.47005   0.35572   1.2611   -0.67581  -0.94983   0.68666
  0.3871   -1.3492    0.63512   0.46416  -0.48814   0.83827  -0.9246
 -0.33722   0.53741  -1.0616   -0.081403 -0.67111   0.30923  -0.3923
 -0.55002  -0.68827   0.58049  -0.11626   0.013139 -0.57654   0.048833
  0.67204 ]
"""  

So we got numerical representation of word ‘hello’.
We can use also pandas to load GloVe file. Below are functions for loading with pandas and getting vector information.

import pandas as pd
import csv

words = pd.read_table(file, sep=" ", index_col=0, header=None, quoting=csv.QUOTE_NONE)


def vec(w):
  return words.loc[w].as_matrix()
 

print (vec('hello'))    #this will print same as print (model['hello'])  before
 

Finding Closest Word or Words

Now how do we find closest word to word “table”? We iterate through pandas dataframe, find deltas and then use numpy argmin function.
The closest word to some word will be always this word itself (as delta = 0) so I needed to drop the word ‘table’ and also next closest word ‘tables’. The final output for the closest word was “place”

words = words.drop("table", axis=0)  
words = words.drop("tables", axis=0)  

words_matrix = words.as_matrix()

def find_closest_word(v):
  diff = words_matrix - v
  delta = np.sum(diff * diff, axis=1)
  i = np.argmin(delta)
  return words.iloc[i].name 


print (find_closest_word(model['table']))
#output:  place

#If we want retrieve more than one closest words here is the function:

def find_N_closest_word(v, N, words):
  Nwords=[]  
  for w in range(N):  
     diff = words.as_matrix() - v
     delta = np.sum(diff * diff, axis=1)
     i = np.argmin(delta)
     Nwords.append(words.iloc[i].name)
     words = words.drop(words.iloc[i].name, axis=0)
    
  return Nwords
  
  
print (find_N_closest_word(model['table'], 10, words)) 

#Output:
#['table', 'tables', 'place', 'sit', 'set', 'hold', 'setting', 'here', 'placing', 'bottom']

We can also use gensim word2vec library functionalities after we load GloVe file.

from gensim.scripts.glove2word2vec import glove2word2vec
glove2word2vec(glove_input_file=file, word2vec_output_file="gensim_glove_vectors.txt")

###Finally, read the word2vec txt to a gensim model using KeyedVectors:

from gensim.models.keyedvectors import KeyedVectors
glove_model = KeyedVectors.load_word2vec_format("gensim_glove_vectors.txt", binary=False)

Difference between word2vec and GloVe

Both models learn geometrical encodings (vectors) of words from their co-occurrence information. They differ in the way how they learn this information. word2vec is using a “predictive” model (feed-forward neural network), whereas GloVe is using a “count-based” model (dimensionality reduction on the co-occurrence counts matrix). [3]

I hope you enjoyed reading this post about how to convert word to vector with GloVe and python. If you have any tips or anything else to add, please leave a comment below.

References
1. GloVe: Global Vectors for Word Representation
2. Load pretrained glove vectors in python
3. How is GloVe different from word2vec
4. Don’t count, predict! A systematic comparison of
context-counting vs. context-predicting semantic vectors

5. Words Embeddings