ULMS893 Marketing and Digital Analytics Assignment 2, UOL

Learning Portfolio Tasks 

Learning Portfolio Task 1: “Topic Modelling” (Week 10 Seminar)

This learning portfolio task uses Amazon reviews on the Kodak Printomatic Digital Instant Print Camera, which can be found in the csv file labelled “AmazonKodakReviewPosts.csv”. You can download the file from the “all data sets” folder on Canvas (this folder is in Start Here in the Materials folder).

Task: Identify latent topics in the review data in order to generate insights for Kodak’s product management regarding important features of the camera and, if present in the data, customers’ perceptions of the brand. Use tables/graphs to support your discussion. [Modify the R Studio codes provided for the week 10 seminar to run your analyses.]

Learning Portfolio Task 2: “Sentiment Analysis” (Week 11 Seminar)

This learning portfolio task also uses the same data set of online reviews on the Kodak Printomatic Digital Instant Print Camera (please see task 1).

Task: Analyse customers’ sentiment to generate insights for Kodak’s product management regarding product weaknesses and strengths as well as wider implications for the brand. Use tables/graphs to support your discussion. [Modify the R Studio codes provided for the week 11 seminar to run your analyses.]

Guide to Preparing/Submitting your Portfolio

Word limit: You need to complete both tasks, with approximately 1,000 words allocated to each task. Please do not exceed the maximum word count of 2,000 words overall for this assignment. There is no leeway regarding word count (please see handbook).

What is included in the word count: Please see the module handbook regarding what is included in the word count. However, note the following important exception for this module: All charts/graphs produced in R Studio are not included in the word count. Similarly, simple tables (e.g. showing a list of analysed words or some review excerpts) are not included in the word count. However, if you do include tables or other figures created by yourself with lots of text and explanations which should be in the main body of the learning portfolio, these will be included in the word count (i.e. do not use tables or figures to sneak in overflow text). The same is true for any appendices you wish to include.

Structure and style: Simply structure your portfolio by task. You can have sub-headlines to structure your text for each task. At the heart of your writing should be managerial relevance and a clear communication, as discussed in seminar 1 (week 7). Note that what counts for these tasks are your analytical skills, reasoning/communication and data support of presented findings/recommendations (i.e. show that what you are discussing is backed by your analyses of the data). You will also need to acquire background knowledge of the brand and product in order to make meaningful recommendations. However, you do not need to write an essay and include academic references, as our main focus in this module are analytical skills and problem solving. Please also do not include lengthy explanations of the coding to conduct the analyses. Finally, please write in the third person rather than first person.

Location of tables and figures: When including tables and figures to support your arguments, please place them directly in the text where you discuss them rather than in an appendix. Note that most of the tables/figures you produce are not included in the word count (see above) and they are an important part of your discussion.

Formatting and template: Please use the word template (see Canvas download). Figures and tables should be numbered and appropriately labelled (i.e. choose a brief description of what the figure/table shows). You don’t need a list of figures and tables after the contents page, but if you want to include that you can. If you should have references, such as sources with background knowledge on the brand, please insert them at the end of the learning portfolio (we use the Harvard referencing style at ULMS). You don’t need to reference R Studio for graphs that you have created.

Submission: Please submit your learning portfolio via the relevant resit link on Canvas. Please submit the word file. A hardcopy is not required.