SEMESTER 1 2019/20 COURSEWORK BRIEF:
Module Code:
MANG6331
Assessment:
Individual Coursework
Weighting:
100
Module Title:
Text Mining and Social Network Analytics
Module Leader:
Meko So
Submission Due Date: @ 16:00
10th January 2020 (Friday)
Word Count:
2000
Method of Submission:
Electronic via Blackboard Turnitin ONLY
(Please ensure that your name does not appear on any part of your work)
Any submitted after 16:00 on the deadline date will be subject to the standard University late penalties (see below), unless an extension has been granted, in writing by the Senior Tutor, in advance of the deadline.
University Working Days Late:
Mark:
1
(final agreed mark) * 0.9
2
(final agreed mark) * 0.8
3
(final agreed mark) * 0.7
4
(final agreed mark) * 0.6
5
(final agreed mark) * 0.5
More than 5
0
This assessment relates to the following module learning outcomes:
A. Knowledge and Understanding
B. Subject Specific Intellectual and Research Skills
A1. Some common text mining and social network analytics activities in contemporary organisations; A2. The complexities of collecting, integrating, processing and managing text and network data from a wide range of internal and external sources;
A3. How various analytical techniques can be used to uncover the potential of text and network data to gain actionable insights and support marketing decisions.
B1. Collect, integrate and prepare text and social network data with other types of data collected from various sources;
B2. Apply analytical techniques to analyse text and network data;
B3. Evaluate suitable approaches for a range of analytical tasks related to text mining and social network analytics;
B4. Derive actionable insights through the results of analyses.
3
C. Transferable and Generic Skills
C1. Communicate ideas and arguments fluently and effectively through a written report; C2. Manage yourself, time and resources effectively;
C3. Use computing and IT resources effectively;
C4. Demonstrate confidence in your own ability to learn new concepts.
Coursework Brief:
Assume you are a marketing analyst of British Airway (BA), you are going to provide a report about the potential of using twitter data to conduct different analytic activities. Your manager is going to present the findings of this report to the companys executive board. Your report is going to include the followings:
1. You are going to collect tweets that are associated with the @British_Airways and @VirginAtlantic twitter accounts (covering the period from 1st to 31st December 2019). You are going to (a.) conduct exploratory data analysis with these tweets, and (b.) compare/contrast the tweets of these two airlines;
2. Your manager would like to explore the potential of replacing its customer satisfaction survey by mining twitter data in the future. Evaluate the pros and cons of this suggestion;
3. Your manager would like to develop an in-house model to predict the sentiment of tweets. S/he would like you to conduct a trial on an airline twitter sentiment dataset. You are going to (a.) conduct exploratory data analysis on this dataset, (b.) build two different classification models to predict tweet polarity, and (c.) evaluate the models with suitable performance measures.
Notes:
(i) Your report should communicate your key findings clearly to stakeholders (i.e. your manager who has
good knowledge on various analytics techniques, and the executive board who may not be familiar with the data or some advanced analytical techniques). Briefly indicate what steps you took in your analysis, visualise your results where appropriate, and clearly state your recommendations backed up by your results.
(ii) You could use either R or Python to conduct the analysis. Please submit your programming codes for this assignment. Note that programming codes are excluded from the word limit.
SEMESTER 1 2019/20
(iii) Please refer to the grade descriptor of this coursework for the detailed marking scheme.
(iv) For part (3.), the dataset has already been divided into training and testing sets and you could download
them from the modules Blackboard site. Moreover, in this exercise, you will focus only on predicting positive/negative tweets (i.e. all neutral tweets have been removed).
Nature of Assessment: This is a SUMMATIVE ASSESSMENT. See Weighting section above for the percentage that this assignment counts towards your final module mark.
Word Limit: +/-10% either side of the word count (see above) is deemed to be acceptable. Any text that exceeds an additional 10% will not attract any marks. The relevant word count includes items such as cover page, executive summary, title page, table of contents, tables, figures, in-text citations and section headings, if used. The relevant word count excludes your list of references and any appendices at the end of your coursework submission.
You should always include the word count (from Microsoft Word, not Turnitin), at the end of your coursework submission, before your list of references.
Title/Cover Page: You must include a title/ cover page that includes: your Student ID, Module Code, Assignment Title, Word Count. This assignment will be marked anonymously, please ensure that your name does not appear on any part of your assignment.
References: You should use the Harvard style to reference your assignment. The library provide guidance on how to reference in the Harvard style and this is available from: http://library.soton.ac.uk/sash/referencing
Submission Deadline: Please note that the submission deadline for Southampton Business School is 16.00 for ALL assessments.
Turnitin Submission: The assignment MUST be submitted electronically via Turnitin, which is accessed via the individual module on Blackboard. Further guidance on submitting assignments is available on the Blackboard support pages.
It is important that you allow enough time prior to the submission deadline to ensure your submission is processed on time as all late submissions are subject to a late penalty. We would recommend you allow 30 minutes to upload your work and check the submission has been processed and is correct. Please make sure you submit to the correct assignment link.
You will know that your submission has completed successfully when you see a message stating Congratulations your submission is complete. It is vital that you make a note of your Submission ID (Digital Receipt Number). This is a unique receipt number for your submission, and is proof of successful submission. You may be required to provide this number at a later date. We recommend that you take a screenshot of this page, or note the number down on a piece of paper. You should also receive an email receipt containing this number, and the number can be found after submitting by following this guide. This method of checking your submission is particularly useful in the event that you dont receive an email receipt.
You are allowed to test submit your assignment via Turnitin before the due date. You can use Turnitin to check your assignment for plagiarism before you submit your final version. See Viewing Your Originality Report for guidance. Please see the Module Leader/lecturer on your module if you would like advice on the Turnitin Originality report.
The last submission prior to the deadline will be treated as the final submission and will be the copy that is assessed by the marker.
It is your responsibility to ensure that the version received by the deadline is the final version, resubmissions after the deadline will not be accepted in any circumstances.
Important: If you have any problems during the submission process you should contact ServiceLine immediately by email at [email protected] or by phone on +44 (0)23 8059 5656.
SEMESTER 1 2019/20
Late Penalties: Further information on penalties for work submitted after the deadline can be found here.
Special Considerations: If you believe that illness or other circumstances have adversely affected your academic performance, information regarding the regulations governing Special Considerations can be accessed via the Calendar: http://www.calendar.soton.ac.uk/sectionIV/special-considerations.html
Extension Requests: : Extension requests along with supporting evidence should be submitted to the Student Office as soon as possible before the submission date. Information regarding the regulations governing extension requests can be accessed via the Calendar: http://www.calendar.soton.ac.uk/sectionIV/special-considerations.html
Academic Integrity Policy: Please note that you can access Academic Integrity Guidance for Students via the Quality Handbook: http://www.southampton.ac.uk/quality/assessment/academic_integrity.page?. Please note any suspected cases of Academic Integrity will be notified to the Academic Integrity Officer for investigation.
Feedback: Southampton Business School is committed to providing feedback within 4 weeks (University working days). Once the marks are released and you have received your feedback, you can meet with your Module Leader / Module Lecturer / Personal Academic Tutor to discuss the feedback within 4 weeks from the release of marks date. Any additional arrangements for feedback are listed in the Module Profile.
Student Support: Study skills and language support for Southampton Business School students is available at: http://www.sbsaob.soton.ac.uk/study-skills-and-language-support/.
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