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COURSEWORK 2 - Tweet Analysis
Final Report Deadline: Friday 14th May 2021, 4pm
Submission: Electronic submission via the module’s Moodle Site.
1. The Problem Definition:
In this coursework, you have been asked to do an exploratory analysis of the public’s
view of its Covid19 - as seen through the lens of social media (twitter). You are provided
with two sets of data about the keywords “covid victim” and “covid survivor”. As an
analyst, you are interested in understanding whether and how people’s attitudes,
emotion, or opinions may be different when they include these two different keywords in
their posts on social media. You are tasked with:
1. Unpacking those tweets’ contents, and from these providing a breakdown analysis
of people’s views on social media (n.b., this might include an exploration of items
drawn from: prevalence of mentions; engagement; key descriptive words; topics
arising; attitudes or types of users mentioning the keywords; analysis of sentiment
surrounding the tweets; geospatial location of mentions; temporal nature of
mentions; etc. - the choice is open and up to you; you are not expected to cover all
of them in a pilot, exploratory analysis).
2. Identifying a further analysis direction that relates to the outcome of current
tweet analysis.
3. Completing a report (3000 words including all visualizations you select - no
appendices will be accepted) summarizing these analyses.
2. Data Description
You are provided with two sets of data “covid_survivor.csv” and “covid_victim.csv” which
contain tweets posted during April 5th 2021 and April 13th 2021. In covid_survivor.csv, the
tweet posts were crawled with the keywords “covid survivor” while in covid_victim.csv the
posts were crawled with the keywords “covid victim”. The features are explained as
follows:
Feature Feature Description
tweet_id The unique identifier of the requested tweet.
user_id The unique identifier of the user who posted this tweet.
user_name The name of the user, as they have defined it on their profile.
user_screen_name
The twitter screen name, handle, or alias that this user identifies
themselves with.
user_location The location specified in the user’s profile. It may not indicate a
valid location.
user_description The UTF-8 text of this user’s profile description.
user_url The URL provided by the user in association with the profile.
user_followers_count The number of followers this account currently has.
user_following_count The number of users this account is following.
user_listed_count The number of public lists that this user is a member of.
user_favourites_count The number of tweets this user has liked.
user_statuses_count The number of tweets (including retweets) issued by the user.
user_created_at The UTC datetime that the user account was created.
user_verified Indicates if the user has a verified account.
tweet_created_at The creation time of the tweet.
tweet_source Utility used to post the tweet, as an HTML-formatted string.
tweet_text The actual UTF-8 text of the tweet.
tweet_lang The using language of the tweet, if detected by twitter.
tweet_favorite_count The number of times this tweet has been liked by twitter users.
tweet_retweet_count The number of times this tweet has been retweeted.
hashtags The hashtag included in the text of a tweet.
mentions Other twitter users mentioned in the text of a tweet.
urls The URL link included in a tweet.
2. Expected Approach:
The data may be analysed in any form you see fit - you may analyse it to evidence reach,
mentions, attitudes, sentiment, geographical patterns, indeed anything you think may be
relevant to understanding the data. However, as detailed above, you must apply some
form of analytics technique during this process, rather than taking a purely qualitative
approach.
3. Report Structure
To present your exploration, you will provide a report of this pilot study that clearly
describes your purpose, the approach and techniques you have taken to underpin your
work, and the results of that analysis (including visualizations where appropriate). You
must also identify at least one potential further analysis direction the company to
engage with (along with some form of justification), before wrapping up your insights in
a final conclusion section. Expected sections are as follows:
1. Executive Summary: including a description of the task, a summary of
your technical approach, a summary of the data that underpins it, a
summary of the results, and a summary of the insights you have arrived at.
2. Approach breakdown: a summary of the process that you have undertaken to
analyze the data, to summarize results, and to draw conclusions.
3. Data Description section: A summary section quickly detailing the data upon
which your analysis is based. This should include at the absolute minimum
including information the number of tweets, the data item used, the number of
unique users analysed, the date range and the geographical area focused on (this
may be global, but please do specify).
4. Analysis section: In this section, which will comprise the bulk of your report, you
must summarize your investigations. Please feel free to split this into different
subsections based on the techniques you examined, or angles you took to
considering the text surrounding tweets, the locations they were produced from,
and the people/companies that were mentioning them.
5. Further Analysis Recommendation: Here you will provide a summary of the
further analysis you recommend to the company to engage with based on current
result of Tweet analysis.
6. Conclusion: A brief conclusion summarising the key parts of your analysis, and any
recommendations you have for the business if they were to extend this pilot study
into a full analysis.
4. Marking Criteria
Your submission will be assessed based on the following mark scheme:
● Executive Summary (5 marks)
● Methodology Section (10 marks)
● Analysis and Description of Results (55 marks)
- Tweet Exploration (40 marks)
- Further Analysis Recommendation (15 marks)
● Conclusion, Insights and Recommendations (5 marks)
● Technical Implementation (20 marks)
- Functionality (15 marks)
- Clarity and Commenting (5 marks)
● Overall presentation and professionalism of Report (5 marks)
5. What you need to submit
⚫ A report on your exploration - 3000 words maximum. See the report section for the
structure and to understand what is expected. The document name should be your
Student ID_Name.
⚫ All code / notebooks / tableau files. I expect at least:
A commented Jupyter notebook containing the code that reloads in these
tweets, and performs analysis on them that you then describe in your
report.
⚫ Supporting data files should be included containing the tweets and any other
data items that you used, that can be run through your analysis script.
6. How to submit
⚫ Submission is electronically via Moodle. The submission link will be made
available shortly.
⚫ DUE DATE: Friday 14th May, 4 pm.
COURSEWORK 2 - Tweet Analysis
Final Report Deadline: Friday 14th May 2021, 4pm
Submission: Electronic submission via the module’s Moodle Site.
1. The Problem Definition:
In this coursework, you have been asked to do an exploratory analysis of the public’s
view of its Covid19 - as seen through the lens of social media (twitter). You are provided
with two sets of data about the keywords “covid victim” and “covid survivor”. As an
analyst, you are interested in understanding whether and how people’s attitudes,
emotion, or opinions may be different when they include these two different keywords in
their posts on social media. You are tasked with:
1. Unpacking those tweets’ contents, and from these providing a breakdown analysis
of people’s views on social media (n.b., this might include an exploration of items
drawn from: prevalence of mentions; engagement; key descriptive words; topics
arising; attitudes or types of users mentioning the keywords; analysis of sentiment
surrounding the tweets; geospatial location of mentions; temporal nature of
mentions; etc. - the choice is open and up to you; you are not expected to cover all
of them in a pilot, exploratory analysis).
2. Identifying a further analysis direction that relates to the outcome of current
tweet analysis.
3. Completing a report (3000 words including all visualizations you select - no
appendices will be accepted) summarizing these analyses.
2. Data Description
You are provided with two sets of data “covid_survivor.csv” and “covid_victim.csv” which
contain tweets posted during April 5th 2021 and April 13th 2021. In covid_survivor.csv, the
tweet posts were crawled with the keywords “covid survivor” while in covid_victim.csv the
posts were crawled with the keywords “covid victim”. The features are explained as
follows:
Feature Feature Description
tweet_id The unique identifier of the requested tweet.
user_id The unique identifier of the user who posted this tweet.
user_name The name of the user, as they have defined it on their profile.
user_screen_name
The twitter screen name, handle, or alias that this user identifies
themselves with.
user_location The location specified in the user’s profile. It may not indicate a
valid location.
user_description The UTF-8 text of this user’s profile description.
user_url The URL provided by the user in association with the profile.
user_followers_count The number of followers this account currently has.
user_following_count The number of users this account is following.
user_listed_count The number of public lists that this user is a member of.
user_favourites_count The number of tweets this user has liked.
user_statuses_count The number of tweets (including retweets) issued by the user.
user_created_at The UTC datetime that the user account was created.
user_verified Indicates if the user has a verified account.
tweet_created_at The creation time of the tweet.
tweet_source Utility used to post the tweet, as an HTML-formatted string.
tweet_text The actual UTF-8 text of the tweet.
tweet_lang The using language of the tweet, if detected by twitter.
tweet_favorite_count The number of times this tweet has been liked by twitter users.
tweet_retweet_count The number of times this tweet has been retweeted.
hashtags The hashtag included in the text of a tweet.
mentions Other twitter users mentioned in the text of a tweet.
urls The URL link included in a tweet.
2. Expected Approach:
The data may be analysed in any form you see fit - you may analyse it to evidence reach,
mentions, attitudes, sentiment, geographical patterns, indeed anything you think may be
relevant to understanding the data. However, as detailed above, you must apply some
form of analytics technique during this process, rather than taking a purely qualitative
approach.
3. Report Structure
To present your exploration, you will provide a report of this pilot study that clearly
describes your purpose, the approach and techniques you have taken to underpin your
work, and the results of that analysis (including visualizations where appropriate). You
must also identify at least one potential further analysis direction the company to
engage with (along with some form of justification), before wrapping up your insights in
a final conclusion section. Expected sections are as follows:
1. Executive Summary: including a description of the task, a summary of
your technical approach, a summary of the data that underpins it, a
summary of the results, and a summary of the insights you have arrived at.
2. Approach breakdown: a summary of the process that you have undertaken to
analyze the data, to summarize results, and to draw conclusions.
3. Data Description section: A summary section quickly detailing the data upon
which your analysis is based. This should include at the absolute minimum
including information the number of tweets, the data item used, the number of
unique users analysed, the date range and the geographical area focused on (this
may be global, but please do specify).
4. Analysis section: In this section, which will comprise the bulk of your report, you
must summarize your investigations. Please feel free to split this into different
subsections based on the techniques you examined, or angles you took to
considering the text surrounding tweets, the locations they were produced from,
and the people/companies that were mentioning them.
5. Further Analysis Recommendation: Here you will provide a summary of the
further analysis you recommend to the company to engage with based on current
result of Tweet analysis.
6. Conclusion: A brief conclusion summarising the key parts of your analysis, and any
recommendations you have for the business if they were to extend this pilot study
into a full analysis.
4. Marking Criteria
Your submission will be assessed based on the following mark scheme:
● Executive Summary (5 marks)
● Methodology Section (10 marks)
● Analysis and Description of Results (55 marks)
- Tweet Exploration (40 marks)
- Further Analysis Recommendation (15 marks)
● Conclusion, Insights and Recommendations (5 marks)
● Technical Implementation (20 marks)
- Functionality (15 marks)
- Clarity and Commenting (5 marks)
● Overall presentation and professionalism of Report (5 marks)
5. What you need to submit
⚫ A report on your exploration - 3000 words maximum. See the report section for the
structure and to understand what is expected. The document name should be your
Student ID_Name.
⚫ All code / notebooks / tableau files. I expect at least:
A commented Jupyter notebook containing the code that reloads in these
tweets, and performs analysis on them that you then describe in your
report.
⚫ Supporting data files should be included containing the tweets and any other
data items that you used, that can be run through your analysis script.
6. How to submit
⚫ Submission is electronically via Moodle. The submission link will be made
available shortly.
⚫ DUE DATE: Friday 14th May, 4 pm.