代做Coca-Cola、代写python设计、python编程调试、代写data留学生

日期：2019-03-18 09:44

Background

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.