Text mining is the application of natural language processing techniques and analytical methods to text data in order to derive relevant information.
Text mining is getting a lot attention these last years, due to an exponential increase in digital text data from web pages, google's projects such as google books and google ngram, and social media services such as Twitter.
Twitter data constitutes a rich source that can be used for capturing information about any topic imaginable. This data can be used in different use cases such as finding trends related to a specific keyword, measuring brand sentiment, and gathering feedback about new products and services.
In this tutorial, I will use Twitter data to compare the popularity of 3 programming languages: Python, Javascript and Ruby, and to retrieve links to programming tutorials. In the first paragraph, I will explaing how to connect to Twitter Streaming API and how to get the data. In the second paragraph, I will explain how to structure the data for analysis, and in the last paragraph, I will explain how to filter the data and extract links from tweets.
Using only 2 days worth of Twitter data, I could retrieve 644 links to python tutorials, 413 to javascript tutorials and 136 to ruby tutorials. Furthermore, I could confirm that python is 1.5 times more popular than javascript and 4 times more popular than ruby.
1. Getting Data from Twitter Streaming API
API stands for Application Programming Interface. It is a tool that makes the interaction with computer programs and web services easy. Many web services provides APIs to developers to interact with their services and to access data in programmatic way. For this tutorial, we will use Twitter Streaming API to download tweets related to 3 keywords: "python", "javascript", and "ruby".
Step 1: Getting Twitter API keys
In order to access Twitter Streaming API, we need to get 4 pieces of information from Twitter: API key, API secret, Access token and Access token secret. Follow the steps below to get all 4 elements:
- Create a twitter account if you do not already have one.
- Go to https://apps.twitter.com/ and log in with your twitter credentials.
- Click "Create New App"
- Fill out the form, agree to the terms, and click "Create your Twitter application"
- In the next page, click on "API keys" tab, and copy your "API key" and "API secret".
- Scroll down and click "Create my access token", and copy your "Access token" and "Access token secret".
Step 2: Connecting to Twitter Streaming API and downloading data
We will be using a Python library called Tweepy to connect to Twitter Streaming API and downloading the data. If you don't have Tweepy installed in your machine, go to this link, and follow the installation instructions.
Next create, a file called twitter_streaming.py, and copy into it the code below. Make sure to enter your credentials into access_token, access_token_secret, consumer_key, and consumer_secret.
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#twitter_streamming.py
# Import the necessary methods from tweepy library
from tweepy.streaming import StreamListener
from tweepy import OAuthHandler
from tweepy import Stream
# Variables that contains the user credentials to access Twitter API
access_token = "ENTER YOUR ACCESS TOKEN"
access_token_secret = "ENTER YOUR ACCESS TOKEN SECRET"
consumer_key = "ENTER YOUR API KEY"
consumer_secret = "ENTER YOUR API SECRET"
# This is a basic listener that just prints received tweets to stdout.
class StdOutListener(StreamListener):
def on_data(self, data):
print(data)
return True
def on_error(self, status):
print(status)
if __name__ == '__main__':
# This handles Twitter authetification and the connection to Twitter
# Streaming API
l = StdOutListener()
auth = OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
stream = Stream(auth, l)
# This line filter Twitter Streams to capture data by the keywords:
# 'python', 'javascript', 'ruby'
stream.filter(track=['python', 'javascript', 'ruby'])
If you run the program from your terminal using the command: python twitter_streaming.py, you will see data flowing like the picture below.
You can stop the program by pressing Ctrl-C.
We want to capture this data into a file that we will use later for the analysis. You can do so by piping the output to a file using the following command: python twitter_streaming.py > twitter_data.txt.
I run the program for 2 days (from 2014/07/15 till 2014/07/17) to get a meaningful data sample. This file size is 242 MB.
- Reading and Understanding the data
The data that we stored twitter_data.txt is in JSON format. JSON stands for JavaScript Object Notation. This format makes it easy to humans to read the data, and for machines to parse it. Below is an example for one tweet in JSON format. You can see that the tweet contains additional information in addition to the main text which in this example: "Yaayyy I learned some JavaScript today! #thatwasntsohard #yesitwas #stoptalkingtoyourself #hashbrown #hashtag".
{"created_at":"Tue Jul 15 14:19:30 +0000 2014","id":489051636304990208,"id_str":"489051636304990208","text":"Yaayyy I learned some JavaScript today! #thatwasntsohard #yesitwas #stoptalkingtoyourself #hashbrown #hashtag","source":"u003ca href="http://twitter.com/download/iphone" rel="nofollow"u003eTwitter for iPhoneu003c/au003e","truncated":false,"in_reply_to_status_id":null,"in_reply_to_status_id_str":null,"in_reply_to_user_id":null,"in_reply_to_user_id_str":null,"in_reply_to_screen_name":null,"user":{"id":2301702187,"id_str":"2301702187","name":"Toni Barlettano","screen_name":"itsmetonib","location":"Greater NYC Area","url":"http://www.tonib.me","description":"So Full of Art |
Toni Barlettano Creative Media + Design","protected":false,"followers_count":8,"friends_count":25,"listed_count":0,"created_at":"Mon Jan 20 16:49:46 +0000 2014","favourites_count":6,"utc_offset":null,"time_zone":null,"geo_enabled":false,"verified":false,"statuses_count":20,"lang":"en","contributors_enabled":false,"is_translator":false,"is_translation_enabled":false,"profile_background_color":"C0DEED","profile_background_image_url":"http://abs.twimg.com/images/themes/theme1/bg.png","profile_background_image_url_https":"https://abs.twimg.com/images/themes/theme1/bg.png","profile_background_tile":false,"profile_image_url":"http://pbs.twimg.com/profile_images/425313048320958464/Z2GcderW_normal.jpeg","profile_image_url_https":"https://pbs.twimg.com/profile_images/425313048320958464/Z2GcderW_normal.jpeg","profile_link_color":"0084B4","profile_sidebar_border_color":"C0DEED","profile_sidebar_fill_color":"DDEEF6","profile_text_color":"333333","profile_use_background_image":true,"default_profile":true,"default_profile_image":false,"following":null,"follow_request_sent":null,"notifications":null},"geo":null,"coordinates":null,"place":null,"contributors":null,"retweet_count":0,"favorite_count":0,"entities":{"hashtags":[{"text":"thatwasntsohard","indices":[40,56]},{"text":"yesitwas","indices":[57,66]},{"text":"stoptalkingtoyourself","indices":[67,89]},{"text":"hashbrown","indices":[90,100]},{"text":"hashtag","indices":[101,109]}],"symbols":[],"urls":[],"user_mentions":[]},"favorited":false,"retweeted":false,"filter_level":"medium","lang":"en"}
For the remaining of this tutorial, we will be using 4 Python libraries json for parsing the data, pandas for data manipulation, matplotlib for creating charts, adn re for regular expressions. The json and re libraries are installed by default in Python. You should install pandas and matplotlib if you don't have them in your machine.
We will start first by uploading json and pandas using the commands below:
import json
import pandas as pd
import matplotlib.pyplot as plt
tweets_data_path = '../data/twitter_data.txt'
tweets_data = []
tweets_file = open(tweets_data_path, "r")
for line in tweets_file:
try:
tweet = json.loads(line)
tweets_data.append(tweet)
except:
continue
print(len(tweets_data))
# we will structure the tweets data into a pandas DataFrame to simplify
# the data manipulation. We will start by creating an empty DataFrame
# called tweets using the following command.
tweets = pd.DataFrame()
# Next, we will add 3 columns to the tweets DataFrame called text, lang,
# and country. text column contains the tweet, lang column contains the
# language in which the tweet was written, and country the country from
# which the tweet was sent.
tweets['text'] = map(lambda tweet: tweet['text'], tweets_data)
tweets['lang'] = map(lambda tweet: tweet['lang'], tweets_data)
tweets['country'] = map(lambda tweet: tweet['place']['country'] if tweet[
'place'] is not None else None, tweets_data)
# Next, we will create 2 charts: The first one describing the Top 5
# languages in which the tweets were written, and the second the Top 5
# countries from which the tweets were sent.
tweets_by_lang = tweets['lang'].value_counts()
fig, ax = plt.subplots()
ax.tick_params(axis='x', labelsize=15)
ax.tick_params(axis='y', labelsize=10)
ax.set_xlabel('Languages', fontsize=15)
ax.set_ylabel('Number of tweets', fontsize=15)
ax.set_title('Top 5 languages', fontsize=15, fontweight='bold')
tweets_by_lang[:5].plot(ax=ax, kind='bar', color='red')
# and the second the Top 5 countries from which the tweets were sent.
tweets_by_country = tweets['country'].value_counts()
fig, ax = plt.subplots()
ax.tick_params(axis='x', labelsize=15)
ax.tick_params(axis='y', labelsize=10)
ax.set_xlabel('Countries', fontsize=15)
ax.set_ylabel('Number of tweets', fontsize=15)
ax.set_title('Top 5 countries', fontsize=15, fontweight='bold')
tweets_by_country[:5].plot(ax=ax, kind='bar', color='blue')
转发自adilmoujahid