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Exhibit 25.1   By tapping into social media conversations, companies can gain valuable insights that drive strategic decisions and enhance brand positioning. Source: Nielsen — Brand Association Map

As illustrated in Exhibit 25.1, by tapping into social media conversations, you can gain valuable insights that drive strategic decisions and enhance brand positioning. This chapter aims to equip you with the skills to acquire and analyze these conversations and other social media data using Python, transforming them into valuable marketing insights. You will explore Python tools for data collection, natural language processing (NLP), social network analysis, and data visualization. By accessing data from major platforms like Twitter (X) and Facebook, you will learn to conduct analyses that inform business decisions. Although coding experience is not required, the chapter will help students become familiar with Python.

Topics covered include:

• Introduction to Social Media and Social Data
• Application Programming Interface (API)
• Web Scraping in Python
• Natural Language Processing (NLP)
• Tokenization, Stemming and Lemmatization
• N-grams, Bigrams and Trigrams
• Social Data Mining in Python
• Text Analytics
• Emotion Analysis
• Sentiment Analysis
• Named Entity Recognition (NER)
• Topic Modelling
• Social Influence
• Social Network Analysis
• Data Visualization
• Time Series Analysis
• Data-Driven Marketing Decisions

Additionally, appendices provide foundational knowledge in Python and guide students through key libraries like Pandas, Matplotlib, and Seaborn for data management and visualization.