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COMPUTATIONAL METHODS FOR TEXT ANALYSIS
BA PROGRAM “SOCIOLOGY AND SOCIAL INFORMATICS”
Kirill Maslinsky 2020
Higher School of Economics — Saint Petersburg 1/14
COMPUTATIONAL METHODS FOR TEXT ANALYSIS BA PROGRAM SOCIOLOGY AND - - PowerPoint PPT Presentation
COMPUTATIONAL METHODS FOR TEXT ANALYSIS BA PROGRAM SOCIOLOGY AND - - PowerPoint PPT Presentation
COMPUTATIONAL METHODS FOR TEXT ANALYSIS BA PROGRAM SOCIOLOGY AND SOCIAL INFORMATICS Kirill Maslinsky 2020 Higher School of Economics Saint Petersburg 1/14 WHY DO YOU NEED IT THE TIME WE LIVE IN 2/14 JUST TO LEARN TO MAKE THOSE
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THE TIME WE LIVE IN
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JUST TO LEARN TO MAKE THOSE PICTURES
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1Just kiddin
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IMAGINED AUDIENCE — ALLEGED GOALS
- Practical type — learn to make those pictures
- Sentimental type — make the machine understand those
texts for me
- Philosopher type — why on earth it works at all?
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IMAGINED AUDIENCE — ALLEGED GOALS
- Practical type — learn to make those pictures
- Sentimental type — make the machine understand those
texts for me
- Philosopher type — why on earth it works at all?
4/14
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IMAGINED AUDIENCE — ALLEGED GOALS
- Practical type — learn to make those pictures
- Sentimental type — make the machine understand those
texts for me
- Philosopher type — why on earth it works at all?
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COURSE GOALS
- provide basic understanding of how
to properly use collections of texts as quantitative evidence,
- and to make this knowledge practical
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COURSE CONTENT
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BREAD AND BUTTER: TOPIC MODELING
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KILLER FEATURE: WORD EMBEDDINGS
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THE ICING ON THE CAKE: SENTIMENT ANALYSIS
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THE ICING ON THE CAKE: SENTIMENT ANALYSIS
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THE ICING ON THE CAKE: SENTIMENT ANALYSIS
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COURSE TOPICS
- Basic word statistics:
- lexical statistics (word frequency distributions),
- distributive semantics (word co-occurrence patterns),
- vector representation of text.
- Methods for supervised and unsupervised modeling:
- dictionary methods,
- document classifjcation and clusterization,
- topic modeling,
- word embeddings,
- language models.
- Applied tasks:
- automating content analisys (extracting theme and topic),
- sentiment analysis,
- information extraction from unstructured text.
- this is a really very boring slide, isn’t it?
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WHAT TO EXPECT
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HOW COURSEWORK WILL BE ORGANIZED
Format:
- OFFLINE — lectures, discussions, student presentations
- ONLINE — practical work (programming exercises), tech
support Content:
- NLP basics
- discussion of several recent articles (understanding
methodology, reproducing parts of it)
- Practicing analysis of textual data (with R)
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EXPECTATIONS
Practical work with real texts in class and at home.
- command line
- mining your own text collection
- R scripts
- bugs in scripts, googling, bugs in scripts again
- seeking and getting help from your peers and course
instructor
- happy end
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WORK IN GROUPS
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WHAT YOU CAN LEARN
- State-of-the-art of natural language processing:
- solved problems
- topical issues and unsolved problems
- Terms:
- a minimal vocabulary of necessary linguistic terms (with
meanings! :))
- appropriate keywords to search for current research and
tools
- Tools:
- Where to apply methods for computational text analysis
and how to interpret their results
- Existing software for text analysis (for Russian and English)
- Existing linguistic resources — dictionaries, corpora,
pre-trained models (for Russian and English)
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GRADING
25% student presentations/paper summaries 10% practical exercises 45% lab works (3) 20% fjnal project (?)
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