INFS5730 – Social Media and Enterprise 2.0
SAS Hands-On Assignment – SAS Visual Text Analytics
In this hands-on assignment, you are required to conduct a textual analysis using SAS Visual Text Analytics and submit a report on Moodle course site through Turnitin. The due date of this assignment is on Week 5, Friday 5:00 pm 1st July 2022 (AEST).
Please note that this assignment is worth 20% of your overall course mark.
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Requirements
The purpose of this assignment is to use SAS Visual Text Analytics to analyse a dataset labelled AmazonAlexaReviews available on Moodle as a CSV file. The dataset consists of 3151 Amazon verified customer reviews of various amazon Alexa products like Alexa Echo, Echo dots, etc. The data also includes star ratings, date of review, and variants of Alexa products. In this assignment, you will use only the customer reviews text, which is named verified_reviews. The verified_reviews variable represents free-form, unstructured customer reviews collected from Amazon’s website.
You are required to conduct a data analysis of Amazon verified customer reviews using SAS Visual Text Analytics in two parts. Part 1 consists of exploring predefined concepts and automatically generated topics to derive insights from the data. Part 2 consists of defining your own custom concepts and custom categories to answer specific research questions.
Your report should have the following components:
• A standard cover page (available on Moodle). • Part1
o PredefinedConcepts(worth20%oftheavailablemarks)–upto600words An exploration of the dataset using TWO (2) relevant predefined concepts. For each selected predefined concept, your answer must include the following:
– An explanation of why you think the selected predefined concept can be relevant to your data analysis.
– A discussion of the findings and the insights that you could unveil from these findings. Include relevant screenshots from SAS Visual Text Analytics.
– A discussion of the benefits and limitations of relying only on the selected predefined concept.
o Auto-generated Topics (worth 20% of the available marks) – up to 600 words
– An exploration of the dataset using TWO (2) relevant topics among those automatically generated by SAS.
For each selected topic, your answer must include the following:
– An explanation of why you think the selected topic can be relevant to your data analysis.
– A discussion of the findings and the actionable insights you could derive from these findings. Include relevant screenshots from SAS Visual Text Analytics.
o Custom Concepts (worth 30% of the available marks) – up to 800 words
Write TWO (2) custom concepts, each using a different concept rule type. For each custom concept, your answer must include the following:
– An explanation of the objectives of your analysis
– A justification of the reasons behind your choice of the concept rule type
– The custom concept rule to fulfil the objectives of your analysis
– A detailed explanation of the concept rule syntax
– A discussion of the findings and insights that you could derive from these
findings. Include relevant screenshots from SAS Visual Text Analytics. o Custom Categories (worth 30% of the available marks) – up to 800 words
Write TWO (2) custom categories.
– An explanation of the objectives of your analysis
– The custom category rule to fulfil the objective of your analysis
– A detailed explanation of the category rule syntax
– A discussion of the findings of your analysis and insights that you could
unveil from these findings. Include relevant screenshots from SAS Visual Text Analytics.
Submission instructions
Please submit a word document to the Turnitin assessment submission link on Moodle.
Late submission will incur a penalty of 5% per hour or part thereof from the due date and time unless special consideration has been approved. An assignment is
considered late if the requested format, such as hard copy or electronic copy, has not been submitted on time or where the ‘wrong’ assignment has been submitted.
Font should be no smaller than Arial 12, with standard margins. The spacing must be 1.5. Please note that material exceeding the word limit for each question will not be considered when grading the assignment. Please also note that screenshots do not count towards the word limit.
Instructions on how to load the data into SAS Visual Text Analytics
• Using your SAS Profile, log in and launch SAS Viya for Learners 3.5 from the VFL launch page: https://vle.sas.com/vfl
• Access SAS Studio in VFL by clicking the Applications menu in the upper-left corner and select Develop SAS code.
• If it is not already selected, click the Explorer tab in the left bar of SAS Studio. Then click the triangle to the left of the file directory icon — it will have a name beginning with pdcesx.
• Right-click casuser, and then click Upload Files. Do not create a subset folder under casuser; instead upload the file directly under casuser.
• A dialogue box will open. Click the (+) plus icon on the right, browse/select the file on your Local drive that you want to upload. Recommended format for Data Set is: .sas7bdat, .sashdat or .csv file in UTF-8 format. The dataset AmazonAlexaReviews.csv provided on Moodle is a csv file in UTF-8 format.
• Add the file as attachment to the dialogue box. Click Upload to upload the file to casuser in the ‘Home’ directory in SAS Studio.
• Data set/file is now available in casuser in the ‘Home’ directory in SAS Studio in VFL and can be found under Data Sources when selecting the dataset for SAS Visual Text Analytics project
• Click on the drop-down arrow on the cas server.
• Click on the drop-down arrow on CASUSER.
• Highlight the data set/file(s) in CASUSER in SAS VA in VFL. Then click on the icon to load the file into memory.
• Data set/file is now available in Memory in CASUSER in SAS VA in VFL.
Marking Criteria
The standard F, P, C, D, HD criteria applies when marking the hands-on SAS assignment:
• Fail (0-49) – unsatisfactory performance, does not demonstrate an ability to use the analytical software SAS Viya Textual Analytics to conduct Textual Analysis, provides inaccurate findings from the analysis, provides inaccurate and /or incorrect interpretation of the key findings as mentioned in the requirements, does not provide a discussion of the actionable insights and lack of justification and support with relevant screenshots taken from the analysis.
• Pass (50-64) – basic performance, demonstrates an acceptable level of understanding of how to use the analytical software SAS Viya Textual Analytics to conduct Textual Analysis, provides a descriptive standard of discussion of the findings with limited critical thinking, basic explanation of the actionable insights, and no justification of arguments.
• Credit (65-74) – good performance, demonstrates the ability to use the analytical software SAS Viya Textual Analytics to conduct Textual Analysis, demonstrates a good analytical and critical thinking, provides good quality work in terms of discussion of the key findings, explanation of actionable insights derived from the findings, and justification of arguments along with support with relevant screenshots taken from the analysis.
• Distinction (75-84) – superior performance, demonstrates excellent ability to use the analytical software SAS Viya Textual Analytics to conduct Textual Analysis, demonstrates the ability to think out of the box along with an excellent analysis from a number of perspectives, excellent explanation and interpretation of the key findings, provides an excellent discussion of the actionable insights derived from the findings along with excellent justification.
• High Distinction (85-100) – outstanding performance, exceeds criteria at an exceptional standard, demonstrates an in-depth comprehensive understanding of the use of the analytical software SAS Viya Textual Analytics to conduct Textual Analysis, demonstrates excellent analytical and critical thinking, provides excellent results-driven from the analysis, provides an exceptional explanation of the key findings, provides an excellent discussion of the actionable insights along with justification and support with relevant screenshots taken from the analysis.
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