As mentioned earlier, sentiment analysis is the process of identifying and extracting sentiment information related to a specified topic, domain, or entity, from a set of documents. The sentiment is identified using trained sentiment classifiers. Thus, the quality and the type of the training data have a big impact on the classifier's performance. Most pre-trained classifiers (like VADER) are trained on general texts because they are designed to be versatile for use on different topics. Unfortunately, when we need to extract sentiment from a specific textual data (for example, very domain specific) such as a general classifier might not perform very well. That is why, it makes great sense to train our own classifier that will fit specific needs, or alternately, just train a general classifier, but based on customized, verified, and known datasets. In short, the magnitude of adaptation to the domain is what makes the difference between a good sentiment analysis...
Python Social Media Analytics
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Python Social Media Analytics
By:
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.
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Current Title:
Python Social Media Analytics
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
Acknowledgments
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Introduction to the Latest Social Media Landscape and Importance
Harnessing Social Data - Connecting, Capturing, and Cleaning
Uncovering Brand Activity, Popularity, and Emotions on Facebook
Analyzing Twitter Using Sentiment Analysis and Entity Recognition
Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured
The Next Great Technology – Trends Mining on GitHub
Scraping and Extracting Conversational Topics on Internet Forums
Demystifying Pinterest through Network Analysis of Users Interests
Social Data Analytics at Scale – Spark and Amazon Web Services
Customer Reviews