Text Analysis: Linguistic Meets Data Science: 1.3. Tools of the trade(s): 1.3.5. Tools of the Trade(s) Quiz

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1.3. Tools of the trade(s)

1.3.5. Tools of the Trade(s) Quiz

◄ 1.2. Data science research perspectives

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Linguistics and Data Science Quiz ►

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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

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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