Data visualization –
Text


The first step
•Text has to be turned into data
•Quantitative coding
•Human coding
•„automatic“ (machine processed) coding
•Almost always inclde some decision of human
•Frequency analysis (Wordclouds)
•Sentiment analysis - Is the text positive or negative?
•Topic models - what topics are in the text(s)
•Analysis of readability (how complex the text is?)
•Analysis of „positions“

•Allocating recording units to substantial categories
•Classifying each coded unit of text from the sample according to the category scheme
•Coding scheme
•Before coding (deductive)
•During coding (inductive)
•Unit: page, paragraph, sentence, quasisentence
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Quasi-sentence
•an argument or phrase which is the verbal expression of one idea or issue
•One sentence may include more ideas, one idea may be divided into more sentences.
•
•I am going to buy bread, milk and apple.
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•I am going to buy bread.
•I am going to buy milk.
•I am going to buy apple.
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Text elements
•Paragraphs
•Sentences
•N-grams
•Words
•Lemma
•token

Problems
•Language
•Tenses, adjective, male/female
•Common words (stopwords)
•Reliability of human coding
•Different understaning by different coders
•Noise in automatic coding
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Wordcloud