MANP006/MKTP026 Text Analytics for business/marketing University of Stirling
01 Dec 2023Share via Whatsapp
The objective of this assignment is to apply text analytics
techniques on a dataset of containing public tweets scraped from X (formerly Twitter)
to extract valuable insights and sentiments. Students will preprocess the text
data and perform descriptive analytics (word cloud and concordance) and
predictive analytics such as sentiment analysis, and topic modeling to identify
key topics discussed in the reviews.
Assignment 2: Individual
You will be provided with a dataset scraped from X (Twitter). The dataset includes tweets of public(consumers) about lab-grown meat also known as cultured or cell-based meat. Cultured meat is a form of cellular agriculture where meat is produced by culturing animal cells in vitro. The data also contains meta data of users that can also be utilized to generate meaningful insights.
text analytics techniques, analyse the public acceptance of lab-based meat. And
provide actionable insights and recommendations policy makers as well as
companies dealing with such foods for improving their product/services.
About the dataset
dataset contains 91,116 tweets about lab-grown meat scraped from X (Twitter).
csv file contains 12 fields as below:
Preprocess the given dataset to clean the text
data. Include steps such as:
Removing irrelevant characters, numbers, and
Converting text to lowercase.
Tokenization and stop-word removal.
Lemmatization or stemming.
Document the preprocessing steps and rationale
behind each step.
identifying frequently mentioned keywords in the
Visualizing the most common adjectives or adverbs
used to describe a product or service.
Analyzing the usage of a specific terms in the
Understanding the context in which a particular
word is employed to derive its meaning and nuances.
Perform sentiment analysis on the preprocessed text
data to determine the sentiments expressed in the review.
Analyze the distribution of sentiments and
visualize the results using appropriate charts or graphs.
Provide insights into the overall sentiment of the
hotel reviews and any trends observed.
Apply topic modeling techniques (e.g., Latent
Dirichlet Allocation) to identify key topics in the reviews.
Analyze and interpret the topics identified,
providing a brief summary of each topic.
Analysis and Recommendations
Based on the text analytics results, provide
actionable insights and recommendations for whether public would acceptance
such future foods to companies of this industry as well to policy makers.
Support your recommendations with evidence from the
Your report will need to show that you understand the
business (lab-based meat industry in case) and that you are able to demonstrate
a good understanding of the data. You are expected to produce an action plan
for companies (restaurants or lab grown meat producers) involved in such
Your key tasks are:
1. Perform stage 1 of CRISP-DM – Business
Understanding (understanding of business of alternate food industry) that will
justify the analysis from a business perspective. Showing what will be
involved, which of the two topics you will focus on, and why there will be
significant payoff for doing the work.
2. Perform stage 2 of CRISP-DM – Data
Understanding for the hotels that will demonstrate knowledge and understanding
of the data and will present examples of the types of analysis that can be
part of your Data Understanding phase use appropriate text analytics
techniques, such as word clouds, concordance, bag of words, sentiment analysis
and topic analysis to provide examples of the kinds of analysis that are
4. Make recommendations that will deliver to
the hotels/restaurants with an evidence-based rationale for a 5-stage action
plan to improve Business Operations, or to suggest Marketing Operations that
the hotels can use to influence change.
Submit a comprehensive report detailing the
preprocessing steps, word cloud, concordance, sentiment analysis, topic
modeling results. And include visualizations, charts to support your analysis.
need to complete your report of 2500 words by Wednesday 13th of December 2023 (UK time) and it needs to be submitted via the assignment submission link in
the module Canvas site.
Your report will be assessed on five components as
Percentage of Marks Awarded
Quality of Report Presentation, layout, logic, impact
Use of examples in descriptive and predictive analytics to bolster
Overall Competence of Project Proposal
word limit for the report is 2500. There will be a strict penalty for the
report going beyond the +/- 10% of recommended word limit. Please note that the references would
not be counted in the word limit.
Plagiarism is strictly prohibited. Ensure
that all work is original and properly cited. You are also reminded that
evidence of plagiarism may result in disciplinary
Check below the policy on academic
misconduct and make sure that the work submitted is your own: https://www.stir.ac.uk/about/professional-services/student-academic-and-corporate-services/academic-registry/academic-policy-and-practice/quality-handbook/academic-integrity-policy-and-academic-misconduct-procedure/
supplement with the information, it is advised to use an appendix section for any
tables, figures, screenshots, or other materials that help to justify the ideas
presented. These appendices should be mentioned in the body of the report
(referring the reader to the relevant appendix) and organised in the same order
as they appear mentioned in the main document. Note that, the appendix is not a
section to put text that does not fit in the body of the report because of the
count excludes references and appendices.
order to make your analysis more compelling, make sure that your report cites
any sources/references that strengthen your argument and show evidence of your
claims. These sources can be a combination of academic journal articles,
statistics, practitioners’ literature, market research reports, news, etc.
references in the report should be in the reference list and the other way
round (see further information at the end of the assignment brief).
Stirling Management School recommend using the Harvard Stirling University Referencing Style (HSU).
The following brief information will help you to get
started using HSU but you should consult the Harvard Stirling University Guide on the Library web pages (http://libguides.stir.ac.uk/Harvard-Stirling) for more
detailed guidance, additional reference types and updates. This information is also available in the
Management School Undergraduate Student Handbook which is available on Canvas.
acknowledge a paraphrased idea put the reference information in brackets next
to the idea used.
There is some evidence (Smith 1995) that these figures are incorrect.
Smith (1995) has provided evidence that these figures are
Multiple Authors: If a reference has two authors
include both e.g. (Smith and Richardson 2013) but if it has more than two
authors give only the first name followed by et al. e.g. (Johnston et al.
Example Reference List / Bibliography
R.C. and Klofstad, C.A. (2012) Preference for leaders with masculine voices
holds in the case of feminine leadership roles. Plos One. 7 (12),
the Kelpies (2014) [Television Broadcast] BBC 2 Scotland, 6 May.
and Williams, S. eds. (2009) Human resource management. Oxford: Oxford
(2003) Sociology and industrial relations. In: P. Ackers and A. Wilkinson eds. Understanding
work and employment: industrial relations in transition. Oxford: Oxford
University Press, pp. 31-42.
Government (2011) Economic strategy: transition to a low carbon economy. Scottish
Government. Available: http://www.scotland.gov.uk/Topics/Economy/EconomicStrategy/LowCarbon
[Accessed:28 March 2012].
an unexpected journey (2013) [DVD] Directed by Peter Jackson. Los
Angeles: Warner Bros. Pictures.
on all referencing styles can be found here: http://www.stir.ac.uk/is/student/writing/referencing/howto/
Delivery in day(s):