Getting Started with Text Analysis with Python
In person
This course is for people who have coding experience in Python and who are interested in text analysis. Text analysis is the process of systematic examination of textual data to uncover patterns, extract insights, and derive meaningful information, such as sentiment analysis, topic modelling, and named entity recognition. Data preprocessing is a crucial step for the above-mentioned methods to be performed smoothly.
In this course, we will cover a series of steps required for further text analysis, including basic data wrangling using regex, tokenisation, lemmatisation, as well as methods to visualise and understand pre-processed text data. We will use NLTK, one of the most widely used Python natural language processing toolkit, to practice these techniques.
This course will be taught by Joy Lan.
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.
Those who have registered to take part will receive an email with full details on how to get ready for this course.
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.
Level
This workshop requires the following pre-knowledge:
Familiarity with working with Python through notebooks/Google Colab
Familiarity with handling and wrangling data and applying functions
It will be sufficient for students to have taken the Introduction to Programming with Python course.
Learning Outcomes
Understand how text analysis libraries work in Python and what they can and cannot do.
Perform a series of basic text analysis techniques on real-world datasets.
Understand the wrangling steps necessary to prepare texts for distant reading.
Skills
Use Python re and nltk library to clean up messy text data.
Use nltk to apply tokenisation, stemming, lemmatization to text data for further analysis, such as topic modelling, sentiment analysis etc.
Use Altair and word cloud to visualise text data.
Explore More Training:
- Beyond Social Networks: Advanced Uses of Gephi in Humanities Research
- Comparing Sentiment Analysis Models in R
- Digital Method of the Month: Text Analysis
- Modelling Unstructured Data with Bert
- Silent Disco: Introduction to LDA Topic Modelling
- Silent Disco: Understanding and Creating Word Embeddings
Return to the Training Homepage to see other available events
Room 4.35, Edinburgh Futures Institute
This room is on Level 4, in the North East side of the building.
When you enter via the level 2 East entrance on Middle Meadow Walk, the room will be on the 4th floor straight ahead.
When you enter via the level 2 North entrance on Lauriston Place underneath the clock tower, the room will be on the 4th floor to your left.
When you enter via the level 0 South entrance on Porters Walk (opposite Tribe Yoga), the room will be on the 4th floor to your right.
