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Visualization of word usage changes over time. Example usage on german covid tweets.

Home Page: https://www.jakob-l-m.de/word-frequency-vis.html

JavaScript 32.72% CSS 9.47% Jupyter Notebook 23.60% Python 27.46% HTML 6.74%
d3 javascript live-demo nlp python temporal tfidf visualization

word_frequency_visualization's Issues

Fix bugs

  • Error: attribute d: Expected arc flag ('0' or '1') - No apparent problem but needs to be fixed
  • coloring after date range change
  • dotted lines should not be over the arcs
  • prevent empty date range selection
  • make stopwords not all lowercase
  • Files with Ä,Ö,Ü,ß cannot be opened. replace with AE,OE,UE,SS

View for a date

  • Create basic view with a list of the words associated with that day
  • Generate Word Clouds
  • use actual weights
  • Make it look nice
  • Animations between Clouds

Animations

Animations for lines, dates, circles etc

  • Lines
  • Dates
  • Circles
  • Frequency Graph
  • word clouds

Plots/Graphs

  • Tweet frequency with label for ÖR and Private
  • Tweet frequency for each profile -> data validation
  • Function flowchart
  • script interactions
  • Tweets sorted by weekdays
  • each profile grouped by month. subplots (4,3)

Github Doku

Create dock for GitHub

  • Main Readme with pics and explanation
  • Readme in folders, explaining file structure

Ausarbeitung

Einleitung (2 Seiten - 2)

  • Motivation
  • Ziele
  • Aufbau

Grundlagen (9 Seiten - 11)

  • Scraping
  • Daten Pipeline
  • Input von anderen Datensätzen
  • Analyse
  • Exportieren der Daten zur Visualisierung

Visualisierung (7 Seiten - 18)

  • Erstellen der Grafiken
  • Animationen
  • Click und Hover Events
  • Farbgebung

Ergebnisse und Evaluation (6 Seiten - 24)

  • Veröffentlichung
  • Probleme der automatisierten Auswahl
  • Probleme an Tweets/Online Sprache

Zusammenfassung und Ausblick (4 Seiten - 28)

  • Möglichkeiten der Erweiterung
  • Abschließende Worte
  • Echtzeit

Anhang

  • Verwendete Bibliotheken mit Anhang

Search Option

  • Textarea to type into
  • Find an efficient algorithm
  • Suggest words that are close
  • Make it pretty

Clean Up

Write comments

  • front end
  • back end

Remove unused code

  • front end
  • back end

Other tasks:

  • transfer notebooks into .py files
  • consistent fonts for website
  • add about section

View for a word

  • Lable x-Axis correctly
  • Calculate Word size by word length
  • Create Graph
  • unpack weights

Preprocessing

  • implement tf-idf
  • use a keyword file
  • update tweet data
  • use a timestamp file for the data base
  • Filter out Tweet replies
  • create an exporter to output json files
  • extract uni, bi and tir grams
  • Use TreeTagger

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