University Marketing Case Study: Analyzing Promotional Tweets

This case study analyzes marketing tweets collected from Twitter, categorizing them into commercial, non-commercial, pro-energy drink, cessation messages, promotional tweets, and embedded association tweets. The study utilizes both manual and computational methods to classify the tweets, presenting examples for each category. It proposes two new categories based on sentiment analysis: Natural Language Approach and Machine Learning Approach. The analysis reveals that brands experience increased audience interaction and involvement on social media platforms, with significant percentages of users responding to brand tweets and exploring brand-related content. The report also discusses managerial implications, emphasizing the importance of social media marketing for organizations. Limitations include negative feedback and potential non-accessibility of certain audiences. The appendices include tables and charts representing the impactful growth in responses in reference to relevant tweets corresponding to different marketing criteria of brands and promotional ideas.