Google Sheets Text Classification Template with Google Cloud Natural Language API for Automated Content Categorization

Manually categorizing thousands of blog posts, articles, social media content, or research documents into topic taxonomies is time-consuming and inconsistent across team members—this automated Google Sheets template integrates Google Cloud Natural Language API’s text classification capabilities to analyze content at scale and automatically assign predefined category labels with confidence scores (0-1 scale) based on Google’s machine learning content taxonomy covering hundreds of topic categories across industries, media types, and subject domains. Created by Lazarina Stoy for MLforSEO, this no-code solution enables content managers, researchers, and digital marketers to process bulk text or URLs through enterprise-grade content classification that categorizes each entry into hierarchical topic labels (like Arts & Entertainment/Movies, Business & Industrial/Finance, Sports & Fitness/Individual Sports) without manual review—transforming unstructured content libraries into organized, searchable taxonomies with quantified confidence metrics indicating classification certainty for quality control and filtering.
The template implements a four-column classification workflow with Google Apps Script automation. Users paste URLs or raw content into the input columns, configure their Google Cloud Natural Language API key in the Apps Script editor (accessed via Extensions > Appscript menu), and execute the classification function to trigger batch processing. The API analyzes each text entry and returns results across structured columns: URL displays the source link, Content shows extracted or provided text for classification, Classification Label reveals the assigned category path using hierarchical notation (parent category/child category/subcategory structure like “Arts & Entertainment/Celebrities & Entertainment News” or “Sensitive Subjects/War & Conflict”), and Confidence presents numerical scores indicating classification certainty (values like 0.9837256, 0.8481474, 0.9469568). The example dataset demonstrates analysis of blog content with diverse topics ranging from education and movies to entertainment news and fashion, each automatically categorized with high confidence scores above 0.80, indicating reliable classification suitable for immediate implementation without manual verification.
Use this for:
‧ Content catalog organization by automatically categorizing large article libraries, blog archives, or knowledge bases into consistent topic taxonomies for improved navigation and discoverability
‧ Content gap analysis by classifying existing content to reveal which topic categories have strong coverage versus underrepresented areas requiring new content development
‧ Social media content categorization by processing posts, tweets, or user-generated content at scale to understand topic distribution, identify trending categories, or segment audiences by interest
‧ Research data organization for academic projects, market research, or competitive intelligence by automatically sorting collected articles, papers, or reports into thematic categories
‧ Editorial workflow automation by pre-categorizing submitted content for appropriate editorial review queues based on detected topic categories
‧ Content recommendation systems by using classification labels to match related content across topic categories for internal linking suggestions or reader recommendations
‧ SEO content strategy planning by analyzing competitor content classification to identify which topic categories they dominate versus opportunities for differentiation
‧ Content performance analysis by correlating topic categories with engagement metrics to identify which content types or subject areas drive best results
‧ Multi-site content aggregation by classifying content from multiple sources into unified taxonomies for content hubs or news aggregation platforms
‧ Quality control filtering using confidence scores to separate high-certainty classifications (above 0.90 for automatic implementation) from low-confidence entries requiring manual review
This is perfect for content operations managers, digital librarians, research coordinators, and content strategists managing large content inventories (500+ pieces) who need systematic categorization beyond manual tagging—particularly valuable when organizing legacy content libraries lacking consistent taxonomies, when scaling content operations where manual categorization becomes a bottleneck, when implementing content recommendation systems requiring accurate topic classification, when conducting content audits to understand topic distribution and identify gaps, or when demonstrating content strategy coverage across subject areas through data-driven category analysis rather than manual content reviews—all automated through Google Cloud’s hierarchical taxonomy covering hundreds of categories with confidence scoring that enables threshold-based filtering for quality assurance.