CET365 Social Media Metrics & Analytics End-of-Course Assessment 2026 | SUSS

CET365 End-of-Course Assessment

(Full marks: 100)

Section A

Part I: (42 marks)

Answer all questions in this section.

Social Media Analytics of Apple Inc. 

Apple Inc. is a global technology company renowned for its innovative hardware, software, and services, including the iPhone, Mac, iPad, and the App Store. Founded in 1976, Apple has grown into one of the world’s most valuable and influential brands, shaping the future of consumer electronics and digital ecosystems.

Analysis of Apple MacBook

In recent years, Apple has fundamentally transformed its MacBook lineup through the introduction of Apple Silicon, beginning with the M1 chip in 2020, followed by the M2 series. These in-house processors mark Apple’s shift away from Intel chips, enabling the company to integrate hardware and software more tightly. The result has been a leap in performance, energy efficiency, and thermal management — with fanless designs in some models and significantly extended battery life becoming standout features.

This transition has reshaped the user experience and influenced public perception of the MacBook. The M1 and M2 chips have been widely praised by consumers and reviewers alike for offering fast, silent, and seamless computing, even in entry-level devices. As Apple continues to evolve its MacBook range, understanding how users respond to these developments — alongside other product aspects like pricing, connectivity, and design — is crucial for informing both marketing strategy and product development.

Exhibit 1. Sentiment Analysis Output (Overall Sentiment Distribution):

Distribution of sentiments expressed in tweets about Apple’s MacBook computers.

Exhibit 2. Aspect-Based Sentiment Analysis (Sentiment by Key MacBook Features): Sentiment breakdown by key features of the MacBook.

Exhibit 3. Named Entity Recognition (NER): The most frequently mentioned entities in tweets.

Exhibit 4. Topic Modelling Output (LDA – 5 Topics Identified): Key topics discussed in tweets, identified through LDA topic modelling.

Exhibits 1–4 present the findings from sentiment analysis, aspect-based sentiment analysis, named entity recognition (NER), and topic modelling related to Apple’s MacBook computers. Based on your review of these results, address the following questions:

Question 1

Interpret the overall sentiment distribution in Exhibit 1 and the aspect-based sentiment analysis in Exhibit 2, to explain customer perception of MacBooks.

(6 marks)

Question 2

Analyse sentiment across product features to determine which aspects Apple should emphasise or improve.

(6 marks)

Question 3

Exhibit 3. Named Entity Recognition (NER): The most frequently mentioned entities in tweets.

Examine the identified named entities to assess the breadth of customer conversations, including any references to competitors or retailers.

(6 marks)

Question 4

Exhibit 4. Topic Modelling Output (LDA – 5 Topics Identified): Key topics discussed in tweets, identified through LDA topic modelling.

Evaluate the topics extracted from tweets to infer key themes of user discussion and suggest priorities for Apple’s product development and marketing.

(12 marks)

Question 5

Formulate 4 actionable marketing or product recommendations for Apple based on your interpretation of the analysis outputs.

(12 marks)

Part II Topic Modelling (18 marks)

Answer all questions in this section.

This assignment explores the application of social media analytics in marketing decision-making, using Twitter data related to Apple Inc. You are required to apply theoretical concepts and use NLTK tools to analyse a pre-collected dataset of tweets provided in the csv file: Apple-Twitter-Sentiment-DFE.csv.

Unlike Section A, the data in this section was gathered several years ago, and therefore the findings may not be consistent with current trends. The dataset was obtained using the Twitter Search API, based on the query: #AAPL OR @Apple. The tweets primarily focus on financial and brand-related mentions of Apple, including references to its products (such as the iPhone, iPad, and iOS), stock performance, investor sentiment, and tech news involving the company.

Question 6

Apply LDA topic modelling to identify distinct themes emerging from the dataset. Analyse the results by examining both the key terms that define each cluster and the underlying tweets (the tweets that represent the topics), in order to label the topics and interpret what they reveal about user concerns or interests.

Submit your work in Python using Jupyter notebook.

(18 marks)

Section B Video Presentation (28 marks)

Answer all questions in this section.

Question 7

Prepare a video recording of the presentation for Section A – Part I: Analysis of Apple MacBook (Questions 1 to 5) of at least 5 minutes but not exceeding 10 minutes. Refer to Canvas T/TG/RESIT course site > Assignments > ECA_VIDEO for the step-by-step guide on how to submit the video.

(28 marks)

Section C – PowerPoint Presentation (12 marks)

Answer all questions in this section.

Question 8

Prepare a set of PowerPoint presentation slides upon which the video presentation is based. Please note that the PowerPoint must be converted to PDF before submission to Canvas.

(12 marks)

—– END OF ECA PAPER —–

The post CET365 Social Media Metrics & Analytics End-of-Course Assessment 2026 | SUSS appeared first on Singapore Assignment Help.