Getting Useful Information Out of Unstructured Text
Let’s say that you’re interested in performing a basic analysis of the US M&A market over the last five years. You don’t have access to a database of transactions and don’t have access to tombstones (public advertisements announcing the minimal details of a closed deal, e.g. ABC acquires XYZ for $500mm). What you do have is access to is a large corpus of financial news articles that contain within them – somewhere – the basic transactional details of M&A deals.
What you need to do is design a system that takes in this large database and outputs clean fields containing M&A transaction details. In other words, map an excerpt like this: Continue reading “Shallow Parsing for Entity Recognition with NLTK and Machine Learning”
A useful snippet for visualizing decision trees with pydotplus. It took some digging to find the proper output and viz parameters among different documentation releases, so thought I’d share it here for quick reference.
Continue reading “Decision Tree Visualization with pydotplus”
A couple months back, I worked on analysis and predictive modeling of US salary given census data. Full Jupyter notebook here, below are some details and some of the more interesting findings.
In general, metadata is below and contains lots of null values (as you might suspect of census data).
Continue reading “Income Analysis – US Census Data”
This project stems from two overarching questions:
Which emotions do politicians most frequently appeal to?
I recently saw a BuzzFeed presentation on, among other things, the virality of BuzzFeed content. A big part of their business relies on understanding what kind of content goes viral and why, so their data science team understandably spends a lot of time not only looking at how a piece of content becomes widely popular, but also looking at the distribution of content types in their most popular pieces of content. Continue reading “Trump Tweet Analysis”
How does front page news track a single topic over a period of time? What’s the media’s attention span for a given story?
In general, many find it surprising how quickly major media outlets shift their attention from one story to another. This is partly a reflection of our own attention spans and appetites, and is partly due to the fact that media organizations are incentivized to be the first to break news; as a result readers are more likely to be bombarded with what’s novel instead of what’s important. Continue reading “Article Classification and News Headlines Over Time”