Sessions at PyCon US 2012 matching your filters

Social Network data permeates our world -- yet we often don't know what to do with it. In this tutorial, I will introduce both theory and practice of Social Network Analysis -- gathering, analyzing and visualizing data using Python and other open-source tools. I will walk the attendees through an entire project, from gathering and cleaning data to presenting results.

SNA techniques are derived from sociological and social-psychological theories and take into account the whole network (or, in case of very large networks such as Twitter -- a large segment of the network). Thus, we may arrive at results that may seem counter-intuitive -- e.g. that Justin Bieber (7.5 mil. followers) and Lady Gaga (7.2 mil. followers) have relatively little actual influence despite their celebrity status -- while a middle-of-the-road blogger with 30K followers is able to generate tweets that "go viral" and result in millions of impressions.

In this tutorial, we will conduct social network analysis of a real dataset, from gathering and cleaning data to analysis and visualization of results. We will use Python and a set of open-source libraries, including NetworkX, NumPy and Matplotlib.

Outline:

• Introduction. Why should we do this? What is the data like? Why is this different from other techniques? What can we learn?
• Centralities: Degree, closeness, betweenness, PageRank, Klout Score
• Beyond Klout Score: Finding communities of interest, finding clusters in networks
• Information diffusion in networks -- how do things go viral?

Learn the basics of natural language processing with NLTK, the Natural Language ToolKit. First we'll cover tokenization, stemming and wordnet. Next we'll get into part-of-speech tagging, chunking & named entity recognition. Then we'll close with text classification and sentiment analysis. You'll walk out with new super-powers and an appreciation of the difficulties of analyzing human language.

This tutorial will be a hands on approach to learning natural language processing using NLTK, the Natural Language ToolKit. We will cover everything from tokenizing sentences to phrase extraction, from splitting words to training your own text classifiers for sentiment analysis. Please come prepared with NLTK already installed so we can dive into the code & data immediately.

Hour 1: Tokenization, Stemming & Corpora

Tokenization & familiarity with corpus readers and models are required knowledge before you can get into the more interesting aspects of NLTK. This first hour will include:

• an overview of modules & data
• loading pickled models
• sentence & word tokenization
• stemming & lemmatization
• an overview wordnet and other included corpora

Hour 2: Part-of-Speech Tagging & Chunking/NER

Using tokenization and a working knowledge of corpus readers & pickled models, we'll dive into part-of-speech tagging and chunking/NER, including:

• using a part-of-speech tagger
• an overview of tags and tagged corpora
• training a custom tagger with nltk-trainer
• using a chunker for phrase extraction and named entity recognition
• an overview of chunked corpora
• training a custom chunker with nltk-trainer

Hour 3: Text Classification & Sentiment Analysis

After using classifiers for training part-of-speech taggers and chunkers, this final hour will explain text classification in greater detail with:

• an overview of classified corpora
• text feature extraction
• an overview of classification algorithms & when to use them
• training a sentiment analysis classifier on movie reviews with nltk-trainer
• using a classifier for sentiment analysis
• hierarchical classification for sentiment analysis
• binary vs multi-label classification

Wrapping Up

Now that you know how to use NLTK to process some of the included English corpora, we'll wrap up by covering:

• non-english corpora included with NLTK
• other Python libraries for NLP
• custom corpus creation