Silent Disco: Introduction to Text Analysis
Online
The amount of textual data available to us grows each day, comprising a huge resource which is potentially of tremendous value. This workshop will introduce the process of extracting meaningful structured data and information from text.
Participants will use Python and NLTK (Natural Language Toolkit) for the retrieval of basic textual information such as:
- Word frequencies
- Plots of frequency distributions
- Common word pairs
- Part of Speech tagging
The workshop will take place via Microsoft Teams in a ‘Silent Disco’ format. Participants will work on the tutorial at their own pace. The facilitator will be available via Teams Chat to reply to any questions that arise during the workshop, and to help with installation, troubleshooting or other issues.
This is an intermediate-level workshop. You will need a basic understanding of Python and how to run Python code. We will assume that you are already familiar with the basics of Python, including variables, functions, and basic data types. No prior knowledge of text analysis is assumed. You can build your familiarity with working with Python and Python Notebooks by attending our Introduction to Programming with Python course.
To attend this course, you will have to join the associated Microsoft Teams group. The link to join the group will be sent to attendees prior to the course start date, so please make sure to do so in advance.
This Silent Disco will be facilitated by Xan Cochran.
After taking part in this event, you may decide that you need some further help in applying what you have learnt to your research. If so, you can book a Data Surgery meeting with one of our training fellows.
More details about Data Surgeries.
If you’re new to this training event format, or to CDCS training events in general, read more on what to expect from CDCS training. Here you will also find details of our cancellation and no-show policy, which applies to this event.
If you are interested in other training on working with unstructured data, have a look at the following:
