TextAnalytics_NEC_All_Weeks_Solved

Using the dataset from week 2, eliminate the stop words and create a visualization of the highest frequency words in both a bar chart and a pie chart. Label your graphs and your axes. Comment your code showing your understanding of what the code is doing line by line.

Find another spam text file (UCI, Kaggle, etc) and compare word frequency using both bar charts scatterplots side by side. Order the bar charts from high frequency to low. Create another visualization to show the ten highest frequency words that appear in both files. Show screenshots of your work. I...

• Using the gutenbergr package, (if the default mirror doesn't work use: hgwells <- gutenberg_download(c(35,36,5230,159), mirror = " • Create bar charts of the top ten words that contribute to the positive and negative sentiment in one of the books. • Produce a Word Cloud of the 10...

• Using the Jane Austen novels, show a term frequency distribution with a separate graph for each book. Comment your code line by line to show what it is doing. • Examine Zipf's law for Jane Austen's novels. Create a single graph of rank v. term frequency using logarythmic scales. Comment you...

Find the most common bigrams in Wells' novels. Show a chart and your code with comments. Show tf-idf frequency in faceted graphs of Wells' novels. Determine how often the word 'not' precedes another word in Wells' novels. Show a table.

Create network graph of bigrams in a Wells' novels. Do not include stop words. Make the links darker the more common the bigram is. Use arrows at the end of the line toward the second word. Colorize the central node. Show a chart and your code with line by line comments. Create a count_bigrams fun...

Create a chart showing the words with the greatest contribution to positive or negative sentiment in the AP articles. Show all the code from the necessary packages untll you can produce the chart. Comment your code line by line. Create charts showing the terms with the highest tf-idf from each of ...

1. Show stacked bar charts of the most common terms within each of 2 topics from the Associated Press articles in the topicmodels package. Color the charts by topic. Comment your code line by line. 2. Show a stacked bar chart showing the words that have a Beta greater than 1/1000 in at least one t...

Using the attached files of around 3200 tweets per person, show a histogram (frequency distribution) of the tweets of both Dave and Julia. Use `UTC` to create the time stamp. Remember that the case of column headers matters. Make a dataframe of word frequency for each of Dave and Julia. Plot the f...

Create time series charts for each tweeter showing how word usage has changed over time. Show for three words. You may have to manipulate a parameter to show Comment your code, line by line. Show a graph for each tweeter revealing the ten words with the highest number of retweets. Comment your cod...

Create a stacked bar chart of usenet message groups showing the number of messages in each group. Add some color to differentiate among the types (letters before the first period). Comment your code line by line. Clean the text and remove stop words. Show words by newsgroup. Apply tf-idf metric. S...

Create bar charts showing the most common words that appear in The War of the Worlds with these four words (affect, denounce, perish, soil) Show charts and your code with line by line comments.