AUC_TMCI_2019

Amsterdam University College -- Text Mining and Collective Intelligence -- Fall 2019

Contents

• Hello World: a first notebooks to check everything is working.
• Lab 1: Fundamentals: variables, built-in data types and structures, syntax, flow control.
• Lab 2: Fundamentals: functions, exceptions, classes, I/O.
• Lab 3: More fundamentals: modules, packages, standard library.
• Lab 4_1: Scientific programming: NumPy, matplotlib.
• Lab 4_2: Regular expressions (only for reference).
• Lab 5: NLP pipelines: sentence splitting, tokenizing, stemming and lemmatizing, part-of-speech tagging.
• Lab 6: Web scraping and APIs.
• Lab 7_1: Distributions in texts.
• Lab 7_2: WordNet (only for reference).
• Lab 8: Vector Semantics.
• Lab 9: Intro to ML: linear regression, logistic regression, SGD, Sklearn.
• Lab 10: Word Embeddings: Word2Vec using Gensim.
• Lab 11: Sentiment Analysis.

Set-up

1. Clone the repository locally: git clone https://github.com/Giovanni1085/AUC_TMCI_2019.git
2. Get updates (from time to time): git pull
3. Create a conda environemnt: conda create -n myenv python=3.7 anaconda (where myenv is the envirnoment name)
4. Activate it: conda activate myenv
5. Install packages (see the requirements.txt file), e.g. conda install pandas
6. Launch a Jupyter notebook: jupyter notebook

• More on conda enviroments
• Conda cheatsheet
• Getting started with Jupyter notebooks
• On using git and GitHub for version control

Alternatively, use Binder (link above).

A more detailed guide to setup your environment, with multiple options.

Projects

See the projects folder.

Credits

• Michael Repplinger, who ran the 2018/19 edition and Gianluca Lebani, who ran the 2017/18 edition.
• Giovanni Colavizza and Matteo Romanello, Applied Data Analysis course for the Oxford Digitial Humanities Summer School
• James Hetherington and Giovanni Colavizza, Research Software Engineering with Python