Assignent 11

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Computer Science

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Jan 9, 2024

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Assignment 11 1.)  The code is exploring topics in the Associated Press dataset using Latent Dirichlet Allocation (LDA) from the topicmodels library.  This code applies LDA to the Associated Press dataset with 2 topics and sets the seed for reproducibility.  The tidy function is used to convert the LDA results into a tidy data frame. This code extracts the top 10 terms for each topic based on their beta values.

 This code creates a bar plot using ggplot2, displaying the top terms for each topic.  Overall, this script is a comprehensive exploration of topics in the Associated Press dataset using LDA, followed by visualizing the top terms for each topic. The tidy function from the tidytext library is used to convert the LDA results into a tidy data frame. This makes it easier to work with the data and extract relevant information.

 The code groups the tidy LDA results by topic, then extracts the top 10 terms for each topic based on their beta values (indicating the strength of association with the topic). The result is sorted in descending order of beta values.  The code uses ggplot2 to create a bar plot. Each bar represents a term, and the bars are colored by the topic. The plot is faceted, meaning there is a separate facet for each topic. The scale_y_reordered() ensures that the terms are ordered within each facet based on their beta values. #2.

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 This line loads the tidyr library, which is used for data reshaping and tidying. the code reshapes the data to create a wide-format table ( beta_wide ). It first appends "topic" to the topic numbers, then uses pivot_wider to convert the data from long to wide format. The subsequent filter function removes rows where both topic1 and topic2 beta values are below 0.001. Finally, it computes the log ratio of beta values between topic2 and topic1 .  This section identifies the top terms based on the log ratio of beta values between topic2 and topic1 . It groups the data by the direction of the log ratio (positive or negative), selects the top 10 terms based on the absolute log ratio, ungroups the data, and reorders the terms for better visualization. Finally, it creates a bar plot using ggplot2 with log ratio on the x-axis and terms on the y-axis, providing a clear representation of the differences in beta values between the two topics.  In summary, this code is focused on reshaping and visualizing beta values from the LDA results, specifically looking at the log ratio of beta values between two topics and identifying top terms based on this ratio.

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