Besides using spacy there should be no necessary libraries to run the code here beyond the Anaconda distribution of Python.
In this repository I analyze the inaugural address corpus. This textual data source has the uncommon characteristic that it also contains a time component which makes it very interesting for analyzation. In the notebook president_speeches.ipynb I try to develop visualizations which help answering the following questions:
- "What are some common topics in the speeches?"
- "How important were war and peace over time?"
- "What were important words during war and peace peaks?"
spacy, pandas, matplotlib, sklearn
All of the relevant code for the project is located in the president_speeches.ipynb notebook. I will now try to highlight the most important methods of this notebook.
The following two methods are important to transform the text to a suitable representation:
- extract_topics(): extracts the topics (for now its simply extracts nouns)
- create_tfidf(): creates a term frequency-inverse document frequency representation
After the text has been transformed the following methods create the visualizations:
- plot_most_frequent_nouns(): plots the most frequent nouns/topics in the corpus
- plot_war_and_peace_time(): plots the nouns 'war' and 'peace' over the timeline
- plot_words_peek_years(): plots the most important words of some speeches
The main findings of the code can be found at https://pirnerjonas.github.io/2020/04/07/war-and-peace.html