讲解Coca-Cola、辅导python设计、python编程调试、辅导data留学生 辅导Database|解析Haskell程序
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Coca-Cola launched four new Diet Coke flavors in January 2018: Ginger Lime, Feisty Cherry,
Zesty Blood Orange, and Twisted Mango. The new flavors were accompanied by modernized
packaging and a new ad campaign. Due to stagnation in sales with Millennials, Diet Coke
sought to use this campaign as a way to attract Millennial attention.
The account team has asked the analytics team to perform a series of analysis to determine
the effectiveness of the campaign. This includes but is not limited to: identifying areas of
improvement, determining whether the target audience was reached, and evaluating how well
their set of product influencers performed.
As a data analyst, you are asked to use python(2or3) to perform the following data
analysis to support business team. Your final deliverables will include: spreadsheet(csv)
files reflecting all the work you would have done and notebooks with your code to
generate the csv files.Analysis
Use Diet Coke Raw Data.csv for the following analysis.
Section 1 Metrics
1. We define “social engagement” as the sum number of “likes”, “comments” and
“retweet”. Identify the volume of mentions and total engagement for each month for
each platform and output the result in csv format.
2. Only keep the mentions from Twitter, Instagram, Facebook, News, Blogs and Forums,
and group all the related mentions into 3 categories: 1) Social, 2) News, 3) Blog &
Forum. Then try to identify which category has the highest sentiment and what’s the
total number of positive mentions of that group? Output the result in csv format.
3. Find the top 10 authors with the highest followers for each social platform. Business
analyst also want a Url link to authors' profile or mention page, help them on that as
well. Output the result in csv format.
4. List all the news websites. (i.e. NYTimes)
Section 2 Topic Analysis
1. Write functions to find the official posts for each brand. Add a new column called
“Official” and tag the brand name for each official mention. All the official handles can
be found in Brand Official Handle Keywords.csv.
2. Following step 1, tag all the mentions left as “UGC”(User Generated Content). Write
functions to identify topic for each UGC mention in only “Social” category by using
Emotions Keywords.csv. (The first row of the file is the topic name, from the second row
and below are the keywords for each topic) Tag topic name for each mention being
matched.
○ (Hint) It is possible that some mentions can be matched by multiple topics. So
you need to keep all matched topics.
○ (Hint) Try to use regular expression. Explain the advantages and disadvantages
of regular expression.○ (Hint) Make sure to take care of case sensitivity.
3. Find the number of mentions, total engagement and positive sentiment percentage for
each topic.
Section 3 Longform Analysis
In this section, you will be asked to complete the topic analysis and sentiment analysis on
longform posts. We define all the mentions from News, Blogs, and Forums to be longform
posts. The difficulty of dealing with longform texts is one text can talk about different
topics in different segments. To illustrate, take a look at the example below. This piece of
text talks topic “Surprise” twice. The first appearance is captured by “thrill” and second by
“amazing”. Each highlighted part correspond to one segment.
In order to calculate the sentiment as accurate as possible, we are going to do the
following:
1. Segment the post into individual sentences.
2. Match each sentences against keyword.
3. The matched sentence with 2 sentences before and 2 after combined will be marked
as “key content”.
Look back at the example above, this is how the example is generated.
Similar as section 2, in this section, we want you to write functions to complete the following
steps.1. Classify topics and attach key contents
1.1. Classify topics for all mentions from News and Blog & Forum
categories using Emotions Keywords.csv
1.2. Attach all key contents for each topic in each long form article.
(For instance, create a new column called “key content {topic}”
for each topic in the article. Suppose that there are 5 topics in
one article, you can either append 5 “key content {topic}”
column for this article, or create additional 5 rows, each row
have one different topic and the associated key contents. Each
topic should be treated independently.)
2. Find the sentiment score for every emotioned topic in each longform
articles.
Hint: you may use this https://github.com/youhealthy/vaderSentiment
3. Please included the number of topics matched for each longform article.
Hint: It is possible that some longform articles can be matched by multiple emotion topics.
Section 4 Dashboard
In this section, you are required to complete the data visualization for the following charts in a
Dashboard.
1. Make a line graph to indicate the weekly volume of mentions change in the data set.
2. Use Bar chart to list top 10 websites by volume of mentions for News category
and Blog & Forum categories respectively.
3. Create a bubble chart to show the engagement weekly change in the Social category.
The x-axis would be the week, the y-axis would be the percentage of positive mentions
of the week, and the bubble size would be engagement.
Coca-Cola launched four new Diet Coke flavors in January 2018: Ginger Lime, Feisty Cherry,
Zesty Blood Orange, and Twisted Mango. The new flavors were accompanied by modernized
packaging and a new ad campaign. Due to stagnation in sales with Millennials, Diet Coke
sought to use this campaign as a way to attract Millennial attention.
The account team has asked the analytics team to perform a series of analysis to determine
the effectiveness of the campaign. This includes but is not limited to: identifying areas of
improvement, determining whether the target audience was reached, and evaluating how well
their set of product influencers performed.
As a data analyst, you are asked to use python(2or3) to perform the following data
analysis to support business team. Your final deliverables will include: spreadsheet(csv)
files reflecting all the work you would have done and notebooks with your code to
generate the csv files.Analysis
Use Diet Coke Raw Data.csv for the following analysis.
Section 1 Metrics
1. We define “social engagement” as the sum number of “likes”, “comments” and
“retweet”. Identify the volume of mentions and total engagement for each month for
each platform and output the result in csv format.
2. Only keep the mentions from Twitter, Instagram, Facebook, News, Blogs and Forums,
and group all the related mentions into 3 categories: 1) Social, 2) News, 3) Blog &
Forum. Then try to identify which category has the highest sentiment and what’s the
total number of positive mentions of that group? Output the result in csv format.
3. Find the top 10 authors with the highest followers for each social platform. Business
analyst also want a Url link to authors' profile or mention page, help them on that as
well. Output the result in csv format.
4. List all the news websites. (i.e. NYTimes)
Section 2 Topic Analysis
1. Write functions to find the official posts for each brand. Add a new column called
“Official” and tag the brand name for each official mention. All the official handles can
be found in Brand Official Handle Keywords.csv.
2. Following step 1, tag all the mentions left as “UGC”(User Generated Content). Write
functions to identify topic for each UGC mention in only “Social” category by using
Emotions Keywords.csv. (The first row of the file is the topic name, from the second row
and below are the keywords for each topic) Tag topic name for each mention being
matched.
○ (Hint) It is possible that some mentions can be matched by multiple topics. So
you need to keep all matched topics.
○ (Hint) Try to use regular expression. Explain the advantages and disadvantages
of regular expression.○ (Hint) Make sure to take care of case sensitivity.
3. Find the number of mentions, total engagement and positive sentiment percentage for
each topic.
Section 3 Longform Analysis
In this section, you will be asked to complete the topic analysis and sentiment analysis on
longform posts. We define all the mentions from News, Blogs, and Forums to be longform
posts. The difficulty of dealing with longform texts is one text can talk about different
topics in different segments. To illustrate, take a look at the example below. This piece of
text talks topic “Surprise” twice. The first appearance is captured by “thrill” and second by
“amazing”. Each highlighted part correspond to one segment.
In order to calculate the sentiment as accurate as possible, we are going to do the
following:
1. Segment the post into individual sentences.
2. Match each sentences against keyword.
3. The matched sentence with 2 sentences before and 2 after combined will be marked
as “key content”.
Look back at the example above, this is how the example is generated.
Similar as section 2, in this section, we want you to write functions to complete the following
steps.1. Classify topics and attach key contents
1.1. Classify topics for all mentions from News and Blog & Forum
categories using Emotions Keywords.csv
1.2. Attach all key contents for each topic in each long form article.
(For instance, create a new column called “key content {topic}”
for each topic in the article. Suppose that there are 5 topics in
one article, you can either append 5 “key content {topic}”
column for this article, or create additional 5 rows, each row
have one different topic and the associated key contents. Each
topic should be treated independently.)
2. Find the sentiment score for every emotioned topic in each longform
articles.
Hint: you may use this https://github.com/youhealthy/vaderSentiment
3. Please included the number of topics matched for each longform article.
Hint: It is possible that some longform articles can be matched by multiple emotion topics.
Section 4 Dashboard
In this section, you are required to complete the data visualization for the following charts in a
Dashboard.
1. Make a line graph to indicate the weekly volume of mentions change in the data set.
2. Use Bar chart to list top 10 websites by volume of mentions for News category
and Blog & Forum categories respectively.
3. Create a bubble chart to show the engagement weekly change in the Social category.
The x-axis would be the week, the y-axis would be the percentage of positive mentions
of the week, and the bubble size would be engagement.