Kacie,

I haven't finished this project because I was pulled in a different direction. I hope to restart work early in 2023.

I found the following, which might help you.

Tracking a Descent to Savagery with the Wolfram Language: Plotting Sentiment Analysis in Lord of the Flies https://blog.wolfram.com/2017/12/07/tracking-a-descent-to-savagery-with-the-wolfram-language-plotting-sentiment-analysis-in-lord-of-the-flies/

Analyzing Shakespeare’s Texts on the 400th Anniversary of His Death https://blog.wolfram.com/2016/04/21/analyzing-shakespeares-texts-on-the-400th-anniversary-of-his-death/

I need to count the names of the characters and their number of letters in Shakespeare's "Hamlet", but even after looking at your advice, I don't understand how to do it. For the theoretical part of my assignment, I found https://samploon.com/free-essays/hamlet/ to get details and philosophical explanations of the relationship between Hamlet and Ophelia. However, in addition to these free essays, I need help with the programming exercise.

Daniel,

I was using "12.3.1 for Mac OS X ARM (64-bit) (July 8, 2021)". Tried it on 13.0 PR3 "13.0.0 for Mac OS X ARM (64-bit) (November 12, 2021)" and got the same failure.

Looks like a bug in 13.0.

Daniel,

This works reasonably well on the few that I tried. Will likely need adjusting if the format is different in some of the transcripts. Also does not remove footnote numbers from the text. Will have to parse the HTML to do that.

brixtonLetter[number_] := Module[{plaintext, start, end},
  plaintext = 
   Import["https://russell-letters.mcmaster.ca/brixton-letter-" <> ToString@number, "Plaintext"];
  start = StringPosition[plaintext, "Original"][[1, 2]] + 2;
  end = StringPosition[plaintext, "Notes"][[1, 1]] - 1;
  StringTake[plaintext, {start, end}]
  ]

To get the transcript for letter 20

brixtonLetter[20]

Thanks for that information. Looks like a nice repository but I cannot seem to import the texts of the letters, or even the web site pages, into Mathematica.

The method and training data used by Classify for sentiment analysis is not described anywhere in the documentation as far as I can tell. Apparently, it is trained on social media posts as mentioned here.

The NN repository has this sentiment model trained on Amazon product reviews.

In my experience both perform fairly well on contemporary text. You will just have to try it on your data and manually examine a subset of positive/neutral/negative classifications to convince yourself that it does a good enough job.