Complete Guide to Entity Extraction & Sentiment Analysis: Marketing and SEO Use Cases

Natural language processing sounds abstract until you see exactly how it solves real marketing problems—this detailed Google Sheets guide maps specific NLP techniques (entity extraction and sentiment analysis) to practical SEO and marketing applications with concrete implementation details. Created by Lazarina Stoy for her Introduction to ML for SEOs course, this resource answers the critical “why would I use this?” question by connecting each NLP method to actual business objectives, complete with the questions you’re trying to answer, the input data you need, and where to find it.
The guide organizes use cases across three main NLP approaches: Entity Extraction (Named Entity Recognition), Sentiment Analysis, and Entity Sentiment Analysis (combining both). Each row presents a complete workflow: the marketing challenge (like “Customer persona development” or “SERP feature analysis”), the specific questions you need answered (“What do customers value most?” or “What entities appear frequently in SERPs?”), the type of input data required (short-form text, long-form text, etc.), and the exact data sources to use (customer reviews, competitor content, SERP data, social media posts). Color-coding helps you quickly identify which NLP technique applies—blue for pure entity extraction tasks, orange for sentiment analysis, and purple for combined entity-sentiment approaches that reveal not just what’s being discussed, but how people feel about it.
Use this for:
‧ Identifying exactly which NLP technique solves your specific marketing challenge without wading through technical documentation
‧ Understanding the difference between entity extraction and sentiment analysis and when to use each (or both together)
‧ Building data-driven customer personas by extracting entities and sentiments from reviews, surveys, and social media
‧ Optimizing content strategy by analyzing which entities and topics drive positive sentiment in your niche
‧ Improving SERP visibility by identifying high-frequency entities in search results and understanding their sentiment context
‧ Measuring brand health and competitive positioning through entity-based sentiment monitoring across channels
‧ Creating targeted advertising and personalization strategies based on entity extraction from user behavior data
This is perfect for marketing professionals and SEO specialists who understand they need NLP but don’t know where to start—providing a practical roadmap from business question to implementation approach with specific data sources and use case examples across the customer journey.