Hi Kacie,

Your question seems to be unrelated to this thread which is more about sentiment analysis. You should probably ask a new question.

Anyway, the structure and format of the text of Hamlet that you are using will dictate how to proceed with the analysis. Take a look at my answer to this question. It should help you get started. If you get stuck, ask a new question and include the code you have tried, and explain the problem you have.

Again, thank you to all for the information.

For the letters, I copy and paste the transcription into MS Word so I can edit them. This means deleting the annotation references, poetry, and long quotations. The poetry and quotations could affect the sentiment analysis. Then I copy and paste the edited transcript into a Mathematica notebook.

I have a notebook for each letter, so I just change the file name and the name of the text. The notebook then calculates a work cloud, sentiment analysis for the whole letter, a pie chart for the whole letter, identifies the sentences, plots the sentence positive sentiments (and moving averages), etc.

When I’ve finished all the letters, I’ll use TimeLine to show both sets of letters in order. I’ve not decided on what other information to present, so I enjoy the suggestions and references.

Russell’s 1918 letters from Brixton Prison are available at https://russell-letters.mcmaster.ca/ For each letter there is an image of the original and an annotated transcription. I’m using the transcriptions of the letters in English (some are in French) after removing the references to the annotations. In some cases, Russell quotes passages of text and poems, which I also delete, since they might influence the overall sentiment analysis.

In addition, there is a database of Russell related information at https://bracers.mcmaster.ca/ It doesn’t have transcriptions because of ownership and copyright issues. In the larger project I’m looking at social network analysis related to Russell for 1918. The database, today, returned 1167 entries for 1918.

I’m not sure what to use as training data for a classier. The general consensus is that the writing style in letters is not the same as in published articles and books.

I considered classifying each sentence and letter by hand, but I’m concerned it would be too subjective. Also, since it would take some time, I’m concerned about being consistent.

I like the objectivity from the Mathematica classifier, but given some of the points raised, I may need to adjust some of the classifications.

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]

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.

Yes, this sounds interesting, please keep going. However, you should be skeptical toward whatever the AI outputs, because it's guaranteed not to understand all the nuances of human language, especially in letters from a jailed man to a free woman.

This is an element of a larger project for social network analysis of Bertrand Russell’s correspondence in 1918, the year he went to prison for objections to WWI. The social media at the times was the written letter and Russell was a prolific letter writer.

While in prison he wrote letters and smuggled them out in books he borrowed and returned. My interest here is in the letters he wrote to two women: Lady Constance Malleson, an author and actress, also known as Colette and Lady Ottoline Morrell.

I expect that early in his prison term the letters were largely negative but as the came to an end, they would be more positive. I would also like to compare the sentiment between letters to the two correspondents.

I’m using tools from Mathematica. Many of the ideas came from April 2016 posting Analyzing Shakespeare’s Texts on the 400th Anniversary of His Death.

I certainly appreciate the ideas and look forward to exploring them. If all goes well, I’ll post the results here

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.