Project 01 – Instructions

Due Date: 01 February 2023

General Instructions

This page outlines the instructions for the first project. You should have a file project01.Rmd in your RStudio workspace where you can work on the project. I find that students prefer having a relatively consistent format for the projects, so I will attempt to keep the general format the same throughout the semester.

On the due-date, you or your group is responsible for completing two elements:

1. Slides presenting your results, also submitted in your Box folder. If working in a group, have just one group member submit the slides with all names listed.
2. A short presentation based on your slide show. This should be 3-5 minutes in length.

As described on the syllabus, the project will be graded using a rubric, which can be found here.

Instructions

This project uses a collection of book reviews from the website Good Reads. The data have been taken from the following paper:

Mengting Wan, Julian McAuley, “Item Recommendation on Monotonic Behavior Chains”, in RecSys’18.

Groups will be assigned different genres to look at, so you will have a slightly different analysis compared to the rest of the class. You can read more about the collection as a whole here.

There are two different prediction tasks that you may look at. The first is to classify the score of the book on a scale from 1-5. The second is to predict the gender of the author of the book being described. Here are some things you might consider addressing:

• How accurately can you classify the groups based on the text on the validation data? What kinds of errors does the model tend to make?
• What terms most distinguish the groups? If you look at just verbs or adjectives does anything different pop up?
• Looking at negative example from your best model, do you find it easy to identify the correct class or not?
• If any of the most indicative terms seem surprising, apply KWiC to the data. Does that help explain why the term is predictive?
• Is there a difference in the parts of speech used between the groups?
• Create a confusion matrix and describe any patterns (interesting or not).

At the end of the day, though, your task is an open-ended one. I want you to explore the data using the techniques we have learned so far and then produce a short presentation showing your results to the rest of the class.

Presentation

Your presentation should be in the form of a set of slides. You can build these in whatever software you would like, but please create a PDF version to submit on Box by the project deadline. I will not accept projects in other file formats.

The exact format of the slides is up to you, however there should be a final slide titled “Synthesis” that summarises what you have learned about the data from your analysis. It should take a big picture view of the analysis and not be overly focused on the models.

You will find that most (perhaps all) of the results you want to show are tables and example reviews. Do not use screen shots for these! Screen shots are messy and not ideal. Instead, I suggest using the function dsst_clipboard() to copy information to the clipboard. This can then be pasted into a spreadsheet program or (in some cases) directly into a presentation. For example:

dsst_neg_examples(model) %>% dsst_clipboard()

For visualisations, you should use the function ggsave to store the most recent plot as a JPG or PNG file.

Groups

Groups are assigned different genres to look at in their project. You should be able to download your data set from within the project01.Rmd file.

Group 1: children_b

Group 2: comics_graphic_b

Group 3: fantasy_paranormal_b

Group 4: history_biography_b

Group 5: mystery_thriller_crime_b

Group 6: poetry_b

Group 7: romance_b

Group 8: young_adult_b

Notes

While working through the project, I typically find that many groups ask for help writing the same bits of code. Any notes that I want to share about how to do specific tasks will be added here as we work through the project.