This project performs topic modeling on a collection of US presidential speeches to uncover underlying themes and trends.

Using R and several text mining libraries, this analysis breaks down presidential speeches into topics by:

• Tokenizing speeches into individual words
• Removing common stop words
• Lemmatizing words to their base forms
• Creating document-term matrices for topic modeling
• Applying Latent Dirichlet Allocation (LDA) to identify topics
• Visualizing the most relevant words per topic using ggplot2

• tidyverse
• tidytext
• textstem (for lemmatization)
• topicmodels (for LDA topic modeling)
• dplyr
• stringr

• Load presidential speeches dataset (presidential_speeches_sample.csv)
• Tokenize speeches into single words
• Remove stop words
• Lemmatize words for consistency

• Build document-term matrices representing word counts per speech or year
• Use LDA to discover 12 latent topics across all speeches, by year, and by party affiliation (Democrats and Republicans)

• Extract top words associated with each topic
• Plot relevant terms per topic using bar charts for intuitive analysis

• Part 1: Identify latent topics across all speeches
• Part 2: Analyze frequency of topics by year
• Part 3: Separate analysis for Democratic and Republican speeches

• Uncomment the plot1, plot2, plot_d, or plot_r lines to generate corresponding topic visualizations
• Adjust the number of topics or words per topic in the LDA model as needed for deeper insights

• presidential_speeches_sample.csv contains the speech text and metadata

• The project demonstrates text mining techniques, natural language processing, and visualization applied to political speech data.
• The approach helps reveal thematic shifts over time and across political parties.