--- title: 'Computing Simple Text Similarity (Keywords + Title Overlap, TF-IDF, Cosine Similarity)' description: 'A practical guide for digital marketing and dev teams to implement simple text similarity techniques for SEO, ads, and personalization.' author: 'Gabriel Luis' date: '2025-08-25' tags: ['text similarity', 'TF-IDF', 'cosine similarity', 'SEO', 'digital marketing', 'bag-of-words'] --- import Image from 'next/image'; # The Complete Guide to Computing Simple Text Similarity for Marketing and Web Applications

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Text similarity algorithms for digital marketing and SEO

## Introduction In today's digital world, understanding how similar two pieces of text are is more than a technical curiosity — it's a business advantage. Whether you're: - Comparing blog titles to avoid duplicate content - Matching ads to search queries - Recommending similar products - Performing SEO audits ...text similarity algorithms are behind the scenes doing the heavy lifting. At our digital marketing and development agency, we leverage these algorithms to improve targeting, personalization, and automation for our clients. This guide walks through three simple yet powerful techniques for measuring similarity: 1. Keyword & title overlap (fast, intuitive) 2. TF-IDF (term frequency–inverse document frequency) 3. Cosine similarity on bag-of-words (vector-based comparison) --- ## Part 1 — The Basics of Text Similarity Before the math, let's ground in the business problem. Imagine you run an e-commerce store selling laptops. Two customers search for: > Best laptop for gaming > Top gaming notebook 2025 From a human perspective, those queries are almost identical. A good search system should treat them similarly. Text similarity algorithms help computers do exactly that. --- ## Part 2 — Method 1: Keyword & Title Overlap **Concept:** Count how many words overlap between two text snippets and use that as a similarity score. **Why it works for agencies:** - Extremely fast — can be run in bulk for SEO audits - Great for filtering obvious duplicates in content pipelines **Steps:** 1. Tokenize (split text into words) 2. Remove stopwords ("the", "and", "for") 3. Lowercase all words 4. Count how many are in both sets 5. Divide by the number of unique words ```python from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS def keyword_overlap(a, b): words_a = set(a.lower().split()) - ENGLISH_STOP_WORDS words_b = set(b.lower().split()) - ENGLISH_STOP_WORDS return len(words_a & words_b) / len(words_a | words_b) t1 = "Best laptop for gaming" t2 = "Top gaming notebook 2025" print(keyword_overlap(t1, t2)) ``` **Output:** ```text 0.25 ``` **Pros:** - Simple, explainable - Great for title/tag matching **Cons:** - Doesn't handle synonyms well - Treats all words equally --- ## Part 3 — Method 2: TF-IDF (Term Frequency–Inverse Document Frequency) **Concept:** Not all words are created equal. "Gaming" is usually more informative than "best." TF-IDF scores each word based on: - How common it is in a document (term frequency) - How rare it is across all documents (inverse document frequency) **Formula:** $$ TF\text{-}IDF(t, d) = TF(t, d) \times \log\left(\frac{N}{df(t)}\right) $$ Where: - $t$ = term - $d$ = document - $N$ = total number of documents - $df(t)$ = number of documents containing $t$ ```python from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity docs = [ "Best laptop for gaming", "Top gaming notebook 2025" ] vectorizer = TfidfVectorizer(stop_words="english") tfidf = vectorizer.fit_transform(docs) similarity = cosine_similarity(tfidf[0:1], tfidf[1:2]) print(similarity[0][0]) ``` --- ## Part 4 — Method 3: Cosine Similarity on Bag-of-Words **Concept:** Convert each document into a vector of word counts (bag-of-words) and measure the cosine of the angle between them. **Why cosine?** - If two vectors point in the same direction, they're similar - Ignores absolute length (good for varying text sizes) **Formula:** $$ \cos(A, B) = \frac{A \cdot B}{||A|| \cdot ||B||} $$ ```python from sklearn.feature_extraction.text import CountVectorizer from sklearn.metrics.pairwise import cosine_similarity docs = [ "Best laptop for gaming", "Top gaming notebook 2025" ] vectorizer = CountVectorizer(stop_words="english") bow = vectorizer.fit_transform(docs) similarity = cosine_similarity(bow[0:1], bow[1:2]) print(similarity[0][0]) ``` --- ## Part 5 — Real-World Agency Applications ### SEO & Content Marketing - Detect near-duplicate blog posts before publishing - Cluster related keywords for pillar pages - Audit meta descriptions for uniqueness ### Paid Advertising - Match ad copy to search queries dynamically - Group similar ads to improve A/B testing efficiency ### E-Commerce - Power "similar products" recommendations - Unify different naming conventions in product catalogs --- ## Part 6 — Limitations and Next Steps These methods are effective but surface-level. For deeper semantics, consider embeddings (Word2Vec, GloVe), transformers (BERT), and semantic search pipelines. Use simple methods for filtering, deduping, and quick wins; escalate to embeddings when nuance matters. --- ## Part 7 — Visualizing Similarity for Clients Clients understand faster with visuals: - Venn diagram of overlapping keywords - Heatmap of similarity scores between pages - 2D scatter plot of content clusters (via dimensionality reduction) --- ## Conclusion Text similarity isn't just for engineers. In the hands of marketers, it's a strategic lever for: - Increasing visibility - Avoiding content cannibalization - Enhancing personalization Start with keyword overlap, TF-IDF, and cosine on bag-of-words to unlock data-driven growth strategies that scale. > 💡 _Pro Tip:_ These methods are a great starting point. But when you’re ready to go deeper, > explore **semantic similarity** with modern embedding models for even more accurate matching.

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