Reference no: EM133508393
Customer Analytics and Social Media
Assignment - Social Media Analysis for Understanding Customer Preferences and Sentiments
Learning Objective:
The learning objective of Assignment 1 is to further develop your understanding and skills in social media analytics via performing analysis on two case studies:
1. Case Study A: you will work as a social marketing analyst in a consulting company to uncover the impacts of online advertising and communication with customers. The study aims to educate the marketing teams of their clients (in diverse industries) to market their products and/or services on social media to maximize customers' involvement (positive interest and sharing). The company is interested in finding out the relationship between the comments, headlines, and times, and whether there is a relationship in different topic categories such as entertainment, technology, sports, etc. that are of interest to different clients in various industries.
2. Case Study B: you will be a data scientist working for Netflix to help identify and introduce more potentially popular and good movies. To achieve this goal, you will need to analyze movie reviews based on the IMDB review dataset to help understand user preferences based on SAS Enterprise Miner (covered in the workshop activities on Week 4).
Case Study A
Leveraging the power of content and social media marketing can help elevate the audience and customer base in a dramatic way. However, using social media for marketing without any previous experience or insight could be challenging. It is vital for a marketing team to understand social media marketing fundamentals. If a company publishes exciting, high- quality content and builds an online audience of quality followers, they can comment on it and further share it with their own follower audience on Twitter, Facebook, LinkedIn, Google+, their own blogs, and many other social media platforms. This commenting and sharing of content opens up new entry points for search engines like Google to find it in a keyword search. Those entry points could grow to hundreds or thousands or more potential ways for people to find a company, product, or service online. Finding and understanding the online influencers in the market who have quality audiences and are likely to be interested in the product, service or business could make a huge positive impact.
The consulting company collected information on articles that were shared by people on social media. The dataset contains approximately 12273 articles and a large number (with a total of 18) of features were extracted from the HTML code of the article, including the headline and the abstract of each article. The description of the dataset is provided as an appendix on the last page. Some of the features depend on the characteristics of the service used, which could be analyzed based on the meta-data provided: articles have the meta- data, such as keywords, article domain type and the total number of comments, etc. The article domain categories are: ‘Lifestyle', ‘Scitech', ‘Entertainment', ‘Sports', and ‘World'. In addition, several natural language processing features were also extracted.
Task Requirements
As a data analytics team member for the consultancy firm, you are required to carry out a number of data analytics tasks for the consulting company using the data collected. You are given access to a sample of the data where some of the variables have been removed as they are not considered important for the analysis of this assignment.
The company is interested in examining each article domain to:
• Investigate the impact of the article properties on the number of comments;
• Use the SAS Enterprise Miner for text analysis to identify key features in the articles and analyse their contribution to the low and high number of comments.
To achieve the above, you need to carry out the following data analytics tasks:
a) Task 1: Explore the impact of news properties
Explore the data and investigate what properties of the news correlate with the high number of comments on social media.
• Open the dataset ‘news.csv' using Microsoft Excel.
• Explore the dataset to understand and manage five news categories (Lifestyle, Entertainment, SciTech, Sports, and World) and the associating data.
• Copy the separate datasets for each news category to different Excel sheets (sort and filter by each news category to separate in Microsoft Excel).• In each news category, filter the news with a high number of comments (top 30% in the dataset). Then, investigate the following properties from the filtered data for each category and explain how they could affect the high number of comments. You should provide explanations to support your argument.o Number of words in the headlineo Number of words in the contento Number of keywords in the metadatao The day of the week that the news publishedo The month that the news publishedo Was the article published on the weekend(Hint: To do this, you can create plots in Microsoft Excel or SAS Enterprise Miner with proper measures between the corresponding columns and the number of comments. )
2. Task 2: Use SAS Enterprise Miner for keyword and topic analysis
• Use the SAS Enterprise Miner to extract the keywords from the abstract in each news category. (Hint: To do this, you can refer to the workshop activities in Week 3; by setting the ‘Abstract' column as the only ‘Text' role in the variable setting.)
• What are the highly used (top 5) topics in each category? Use the SAS Result window to explain your answers.
(Hint: the ‘Abstract' column will need to be set as the only ‘Text' role.)
• Use the whole dataset (all categories) in the SAS Enterprise Miner to identify if there are common topics that span across news categories and relate to a high number of comments and a low number of comments. You should provide explanations to support your argument.
(Hint: Use the whole dataset to identify the news with the high number of comments and identify the news with the low number of comments - by using appropriate thresholds with the top 20% and the bottom 20% in the dataset, respectively. Separate the dataset using Excel based on this before the analysis and use these two datasets to analyze the common topics in each of them. In this question, please use the ‘Abstract' column as the only ‘Text' role for topic modelling.)
You are required to:
a) Prepare a report for Case Study A with all the analytics results for the above two key tasks. (You can use an appendix for any additional screenshots, figures, and tables, which you feel are important for the report). The report should be named as:
<student_id>Assignment1_Report.doc We provide a report template for this assignment, and you can follow the template to write up the report.
b) Save the Excel or SAS spk file after Task 1 as: <student_id>Assignment1A.xlsx or
<student_id>Assignment1A.spk
c) Save the SAS project for Task 2 as <student_id>Assignment1_Task2.spk. You may zip the SPKs files if you have multiple of them. The detailed procedures for exporting a model package spk file can be found in Assignment 1 Additional Technical Support file. The SAS project file should be named as:
<student_id>Assignment1_SAS1.zip
Case Study B
Sentiment analysis is the technique aiming to gauge the attitudes of customers in relation to topics, products, and services of interest. It is a pivotal technology for providing insights to enhance the business bottom line in campaign tracking, customer-centric marketing strategy, and brand awareness. In this assignment, you are required to perform sentiment analytics to gain a better understanding of customer preferences.
Task Requirements
As a data scientist, you are required to perform a number of data analytics tasks. You are tasked to analyze IMDB movie reviews from a sample of data using SAS Enterprise Miner.
To achieve the above, you need to carry out the following data analytics tasks:
1. Create a sentiment analysis project in SAS Enterprise Miner. Import the IMDB review dataset (‘Reviews_IMDB.csv'), and explore the dataset.
• What is the most popular rating in the review dataset?
• What is the least popular rating in the review dataset?
2. Apply the Text Profile node, and answer the following questions.
• What are the most used terms for rating 1? And what are the most used terms for rating 10?
• Are there any connections between ratings?
3. Apply the Text Topic node, and answer the following questions.
• What are the top 5 most popular topics in the reviews?
• What topics are most relevant to the rating of 7? To the rating of 8? And the rating of 4?
You are required to:
a) Prepare a report for Case Study B with all the analytics results for the above three key tasks. (You can use an appendix for any additional screenshots which you feel are important for the report). You need to create a new session in the existing Case Study A report. We provide a report template for this assignment, and you can follow the template to write up the report.
b) Save the SAS project for as <student_id>Assignment1B.spk. You may zip the SPKs files if you have multiple of them. The detailed procedures for exporting a model package spk file can be found in the Assignment 1 Additional Technical Support file. The SAS project file should be named as:
<student_id>Assignment1_SAS2.zip