ANL312 Text Mining And Applied Project Formulation End-Of-Course Assessment | SUSS

ANL312 Section A (100 Marks)

Answer all questions in this section.

Question 1

Guidelines for this End-of-Course Assessment report are as follows:

1. Draft a report on your proposed topic, focusing on materials and sources that substantially surpass the content covered in this course or any other ANL courses. The topic should revolve around the practical application of text mining in a specific field or industry.

2. Construct a text mining project based on your selected topic.

3. You can choose either one of the following two options:

Option 1: Apply text categorisation using the IBM SPSS Modeler

You will need to find a dataset with minimum 100 rows of text records for this option. Your response should include documentations of the effort put into improving the resource template and creating the categories from scratch (refer to the GBA steps). Provide screenshots of the text (5-8 samples) for each category, showing the effort put in to correctly categorise the text. Do not use the ‘Build Categories’ feature that automatically builds the categories as no credit will be given if this is done. Do not use “Text Analysis Package”.

Option 2: Apply topic modelling using R programming

You have the option to include sentiment analysis, but this is not mandatory. You will need to find a dataset with minimum 500 rows of text records for this option. Using other software, e.g., IBM SPSS Modeler, for necessary data preparation is allowed for this option.

For topic modelling, please ensure the following:

• Customize your stop words.
• Try a few different values of k and document the process in your answer.
• Evaluate the appropriateness of your final topic model and show screenshots of the top 5 texts for each topic discovered from your topic model in your report, not in appendix. To do this, sort the gamma values of each topic in the gammaDF table, and refer to the text in your original dataset using the doc id.
• Name each topic appropriately.
• More credits will be given if you improve the model by incorporating additional steps beyond the example given in the Study Guide.

Regarding the optional sentiment analysis part, if you choose to include it, follow these guidelines:

• Describe the R package you selected for this project (if different from the package used in the Study Guide).
• Evaluate the appropriateness of the sentiment score generated by the R package you selected, based on the text you collected.
• Include appropriate screenshots of the texts after sentiment analysis and their corresponding sentiment score/polarity. Present the screenshots with the first 20 rows in your report, not in appendix.

Please note that sentiment analysis is optional. Students may or may not earn more credits by including sentiment analysis, depending on the model quality.

Report Requirements

• Length: 3,000 to 5,000 words (excluding cover page, table of content, reference and appendices).
• Marks deducted if below 3000 words. Content beyond 5000 words will not be graded.
• Font size 12, Times New Roman, 1.5 lines spacing.
• Reference citation and reference list: Use APA referencing style.
• Acknowledge and reference all sources used. Up to 25 marks penalty for poor paraphrasing.

Note that Wikipedia can be used as your initial source of information but not as a reference.

• Please write between 3,000 to 5,000 words (excluding cover page, table of content, reference and appendices). Marks will be deducted for those that are below 3000 words. For those reports that exceed 5000 words, only the part that is below 5000 words will be graded and the rest will be ignored. No word limit for individual section.
• Font size 12, Times New Roman, 1.5 lines spacing.
• Reference citation and reference list: Use American Psychological Association (APA) referencing style. Please refer to ANL312 Study Unit 1 for details.
• You must acknowledge and reference all sources used.

Up to 25 marks of penalties will be imposed for inappropriate or poor paraphrasing. For serious cases, they will be investigated by the examination department. More information on effective paraphrasing strategies can be found         on 

Topic Selection

1. You are required to select a topic of your choice. A list of topics is provided in Appendix A. You may propose a topic that is not on the list. However, your topic must be related to things you have learnt in this course.
2. Once you have selected the topic, conduct research to look for a dataset for your project and references for your Literature Review section.
3. Please note that any “restaurant review” data is not allowed for this year’s ECA.

The report must include the following sections, each carrying a specific weightage stated in the relevant sub-question.

Question 1a

Introduction: Based on your chosen topic, discuss the project background, business analytics concepts/issue(s), project objective(s) and role of text mining in achieving the objective(s).

(12 marks)

Question 1b

Literature Review: Describe two (2) references (must be research articles from journal/conference/academic report/thesis) that are applications of text mining and relevant to your selected topic. Include the general background of the study in the references, dataset used, details of how the text mining process is applied, as well as relevant findings and conclusions. Discuss the implications of the references to the current project.

(20 marks)

Question 1c

Body: Use the CRISP-DM framework to organize your report. You are required to find a small dataset and build a text mining model to achieve your project objective(s).

(38 marks)

Question 1d

The last two sections of the report include:

Summary: Summarise the key findings, insights, and conclusions obtained from your text mining analysis.

References: List all the sources you cited in your report and follow the APA referencing style.

Additionally, the entire report will be evaluated based on the coherence and balance maintained across all sections. The Introduction should provide a clear motivation for the project. The Literature Review section should thoroughly review materials closely related to the project. In the CRISP-DM section, the steps should be logically presented, demonstrating a sound approach to text mining. The Summary section should effectively wrap up the project, highlighting key findings and insights. The references should be relevant and support the key ideas presented. The writing should be professional, with good and plain English, adhere to all the instructions given.

(30 marks)