Skip to main content
logo
Shibboleth Login   Login as Guest  
Forgotten your username or password?
HomeContact
About
  • The #dariahTeach Project
  • The IGNITE Project
Testimonials
  • Introduction
  • Unit I: Definitions
  • Unit II: Corpus linguistics & Language
  • Unit III: Text analytics and language
  • Unit IV: Applications / workflows
  • Unit I: Definitions

    This first unit provides the background information needed to engage with the contents of the later, more practical units. The focus is on providing an idea of the different perspectives on language that come into play during the OER. The unit also gives a brief overview of different types of tools that can be used for text analytics and introduce the main tool used in this OER: KNIME.


    • 1.1. Linguistic research perspectives Lesson
    • 1.2. Data science research perspectives Lesson
    • 1.3. Tools of the trade(s) Lesson
    • Linguistics and Data Science Quiz H5P
    • Tool quiz H5P
◄IntroductionUnit II: Corpus linguistics & Language►
Skip Course Custom Menu
Course Custom Menu
Introduction
  Text Analysis - Linguistics Meets Data Science
  • Introduction
  • What is Text Analysis and Why Bother?
  • A Dimpah OER
  • Intended Learning Objectives
  • Teaching and Learning Methods
  • Instructions
Unit I: Definitions
  1.1. Linguistic research perspectives
  • 1.1.1. Computational analysis of text
  • 1.1.2. Linguistic research using computers
  • 1.1.3. What is a corpus?
  • 1.1.4. Linguistic perspectives to data science
  1.2. Data science research perspectives
  • 1.2.1. Data Science Research Perspectives
  • 1.2.2. Sample vs. Population
  • 1.2.3. Sampling
  • 1.2.4. Preprocessing of Texts: Stop words
  • 1.2.5. Preprocessing of texts: stemming, lemmatisation, annotation
  • 1.2.6. Linguistics and Data Science Quiz
  1.3. Tools of the trade(s)
  • 1.3.1. Tools of the Trade(s)
  • 1.3.2. Data Science Tools
  • 1.3.3. Corpus Linguistic Tools
  • 1.3.4. Programming
  • 1.3.5. Tools of the Trade(s) Quiz
  • 1.3.6. Our Tool of Choice: KNIME
  Linguistics and Data Science Quiz
  Tool quiz
Unit II: Corpus linguistics & Language
  2.1. Concordances, tagging, and annotation
  • 2.1.1. What are concordances?
  • 2.1.2. Formulating queries
  • 2.1.3. Part of Speech tagging
  • 2.1.4. Manual pruning and classification of corpus results
  2.2. Patterns in language and text
  • 2.2.1. Collocations
  • 2.2.2. Collocation networks
  • 2.2.3. Semantic prosodies
  • 2.2.4. Words, Sentences and Paragraphs
  • 2.2.5. N-grams, lexical bundles and other sequential patterns
  • 2.2.6. Keywords
  2.3. Registers and text types
  • 2.3.1. Registers
  • 2.3.2. Text types
  • 2.3.3. Corpus Linguistics & Language Quiz
  Corpus linguistics and Language Quiz
  2.4. Frequency, distribution and inferential statistics
  • 2.4.1. Frequency
  • 2.4.2. Distribution
  • 2.4.3. From descriptive to inferential statistics
  • 2.4.4. Statistical significance and effect size
Unit III: Text analytics and language
  3.1. Text analytics and language
  • 3.1.1. Text analytics and language
  • 3.1.2. Machine learning and pattern identification
  3.2. Sentiment analysis
  • 3.2.1. Lexicon-based sentiment analysis
  • 3.2.2. Machine learning and sentiment analysis
  3.3. Topic Modelling
  • 3.3.1. Topic modelling
  • 3.3.2. Topic modelling in KNIME
  3.4. Text Classification
  • 3.4.1. Text Classification
  • 3.4.2. Text Classification in KNIME
  • 3.4.3. Authorship Attribution
  • 3.3.4. Text Analytics & Language Quiz
  Text Analytics and Language Quiz
Unit IV: Applications / workflows
  4.1. Sentiment analysis on Twitter
  • 4.1.1. Overview of workflow
  • 4.1.2. Acquiring the Tweets
  • 4.1.3. Performing Sentiment Analysis
  • 4.1.4. Results
  4.2. Topic Modeling Future Stories For Europe
  • 4.2.1. Overview of workflow
  • 4.2.2. Document Preprocessing
  • 4.2.3. Text Preprocessing
  • 4.2.4. Topic Modeling and Visualisation
  • 4.2.5. Results
Skip Navigation
Navigation
  • Home

    • Site pages

      • Tags

      • Search

      • Calendar

    • Courses

      • Text Analysis: Linguistic Meets Data Science

        • Participants

        • Introduction

        • Unit I: Definitions

          • Lesson1.1. Linguistic research perspectives

          • Lesson1.2. Data science research perspectives

          • Lesson1.3. Tools of the trade(s)

          • H5PLinguistics and Data Science Quiz

          • H5PTool quiz

        • Unit II: Corpus linguistics & Language

        • Unit III: Text analytics and language

        • Unit IV: Applications / workflows

Skip Course Custom Fields
Course Custom Fields

Show All

Main Language

en

Other Languages

aa

Publisher Name

dariahTeach

Authors

Jukka Tyrkkö, Daniel Ihrmark

Licence

CC-BY-SA

Publication Date

2023-05-22 16:20:00

ECTS

5

Back

Guest (Log in)