News and Notes from the Clark Library
A recent project I’ve been working on in the Digital Projects Studio has been moving a website built in PHP to Django. To understand why we’d go through the headache of moving the site into Django it’d be good to first understand some of the scope of the project.
When first learning how to integrate my Bootstrap and Django, I wasn’t able to find a quick cheat sheet to reference without visiting different documentation pages. To help others I’ve put together a list below of the tags I used most often. A full list of Django tags and filters can be found here.
A few weeks ago a researcher came to the Digital Projects Studio for help in getting his research out to a larger audience. His project, on Jewish cafes, had a plethora of information ranging from details on the cafes themselves to the cities and the famous people who had frequented the cafes. Some of the cafes had been destroyed during World War II and others are still in existence today. This is the story of bringing the Jewish Cafes project online.
Stanford Natural Language Processing (NLP) group at Stanford University has an open suite of language analysis tools that are available for the public to use. Most of the tools are only available in English but some have been translated into Chinese, Spanish, German, and Arabic. This tutorial will focus on the English tool sets, specifically the Named Entity Recognizer and the Parts of Speech Tagger. This is helpful is being able to pinpoint and extract specific locations / organizations from a text; Or if you wanted to look at the complexity of sentence structure; Or even looking for hesitations in transcripts for english as a second language learners and where they pause the longest. There are various applications to this technology in research and learning.
This post will explore how to analyze multiple texts using Voyant. In the post below I’ll look specifically at word use through the tools that Voyant offers and explain how to use them.
The process of Visualizing Twitter status data can be informative and revealing about connections to your brand or any topic that you might not have known existed. For example, when I was going through the Pure Michigan data to make the visual, I had to choose what to include and what to exclude. I kept coming across the word ‘xe2’ over and over in the data. After some digging, I discovered that there were several photographers that were using the new Fujifilm XE2 camera to photograph Michigan’s natural beauty and tweeting at Pure Michigan in the process.
Continue reading “Visualizing Twitter Status Data with Wordle”
The aim of this blogpost is for a beginner level user to be able to scrap data from Twitter. In this example, I’ll scrap the 20 most recent statuses from @PureMichigan‘s Twitter feed. My end goal of scraping these posts is to find out quickly who has been talking about @PureMichigan on Twitter most recently and what they are saying. You can also use the count feature to pull up to 200 statuses at a time and analyze the content.
Continue reading “Getting Started: Scraping Twitter Data”