Book Image

Python Social Media Analytics

By : Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk
Book Image

Python Social Media Analytics

By: Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk

Overview of this book

Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes.
Table of Contents (17 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Data pull and pre-processing

Once the crawling is finished, we have all the data in the MongoDB database. We can now query the database to put all the posts into a pandas dataframe:

import pandas as pd

from pymongo import MongoClient

client = MongoClient('HOST:PORT')
db = client.teamspeed
collection = db.forum_teamspeed

dataset = []
for element in collection.find():
df = pd.DataFrame(dataset)

At this stage, we will also create a new column called full_verbatim, where we concatenate the subject (thread title) and post content:

df['full_verbatim'] = df.apply(lambda x: x['subject'] + " " + x['post'],axis=1)

There exists a direct link between thread title and post, so the textual data included in both variables might be insightful with respect to a single thought of the forum user. It will help us to capture the broader and contextual meaning of the ideas expressed in forum posts.

Data cleaning

Thereafter, as seen in the earlier chapters, we need to clean and structure...