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  • The #dariahTeach Project
  • The IGNITE Project
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  • Introduction
  • Unit I: Definitions
  • Unit II: Corpus linguistics & Language
  • Unit III: Text analytics and language
  • Unit IV: Applications / workflows

  • Introduction

    Welcome to Text Analytics meet Data Science! Click the introduction below to get started!



    • Text Analysis - Linguistics Meets Data Science Lesson
Unit I: Definitions►
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Introduction
  Text Analysis - Linguistics Meets Data Science
  • Introduction
  • What is Text Analysis and Why Bother?
  • What is 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
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      • Text Analysis: Linguistic Meets Data Science

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        • Introduction

          • LessonText Analysis - Linguistics Meets Data Science

        • Unit I: Definitions

        • Unit II: Corpus linguistics & Language

        • Unit III: Text analytics and language

        • Unit IV: Applications / workflows

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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

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