How to Connect TextRazor to Google Sheets (4 Methods Compared)

The Problem With Getting Sheet Data In and Out of TextRazor

You have a Google Sheet full of raw text — news article snippets, support ticket descriptions, marketing copy, competitor press releases. You need each row analyzed for entities, topics, or both, and the results written back into adjacent columns in a shape your team can actually use.

TextRazor is good at pulling structure out of unstructured text: named entities, IAB content categories, IPTC topic codes, relationship graphs, relevance scores. But feeding a spreadsheet column into a REST API and writing the results back is more work than the NLP itself. The typical flow is to export the column as CSV, write a script that loops over rows and calls the API, parse the JSON response, decide which fields to keep, then paste the output back into the right place and hope the row order survived.

Below are the four common ways teams handle this. Only the last one scales.

Method 1: Manual Copy-Paste

The default. Open the spreadsheet, copy one cell of text, paste it into the TextRazor API explorer or your own test script, read the JSON response, find the entity names and types or topic labels in the nested structure, and paste what you need into the right columns.

For a one-off check, this is fine. But TextRazor's response for a single text can include dozens of entities with types, relevance scores, Wikipedia links, and confidence values — and you're deciding by eye which ones matter. Do that for 200 rows and you've spent an afternoon building a makeshift extraction pipeline from your clipboard.

The thing about NLP data specifically is that the interesting signal is often in the aggregate. One entity mention means little. Two hundred of them, sorted by frequency across documents, starts to tell you something. Manual copy-paste gives you nothing you can aggregate, because the output never lands in a consistent format.

Method 2: Zapier or Make

Both platforms have TextRazor support through HTTP request steps. You can trigger on a new row in the sheet, make the API call, parse the response, and write values back.

Before you get further into how that works — do you know what a nested JSON path is? Have you ever written a key mapping from an API response into individual columns? Do you understand what a Zap multi-step looks like when one of the steps might return an array of variable length? If any of that feels uncertain, you'll save yourself a lot of frustration by skipping to Method 3 or 4.

For those still here: the integration works. TextRazor returns a structured JSON object with entity arrays indexed by position. You authenticate with your API key, configure the HTTP action, pass the text from the sheet as the payload, extract the fields you want, and write them back.

The structural problem is one row at a time.

Sending 200 snippets through a Zap means 200 trigger fires, 200 API calls, and 200 separate writes — each one carrying only the fields you mapped when you built the Zap. If you need the top 5 entities by relevance score across all rows, or a frequency table of entity types, the automation has no way to give you that. Each trigger fires in isolation.

You probably just need the entities in a usable column format and you probably have no idea how to write a Zap that handles variable-length JSON arrays across 200 rows. So you push it to whoever on your team builds these things, and then you're waiting on a Slack reply to find out whether their version parsed the right nesting level.

And if TextRazor adds a field, or you change which taxonomy you're using, you're back in the Zap editor re-mapping fields.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable spreadsheet ↔ TextRazor workflows was a category of add-ons that let you set up column mappings and saved templates. You configured which column held the text, which columns received the output, and which response fields to extract.

That was a real improvement over one-at-a-time API calls. Configs were reusable, runs were consistent, and you didn't have to rebuild the mapping every time.

But you were still responsible for deciding which fields to extract, mapping each one to a column, writing conditional logic to handle rows where the response returned zero entities, and re-building the config every time you switched taxonomies. The tool moved the data through — the decisions were still yours. And the moment your team needed a different output shape, someone had to go back into the config and rebuild it from scratch.

This is the previous generation. It worked, but it asked a lot of the operator.

The Easy Way: Using SheetXAI in Google Sheets

There is a different way entirely. SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, understands what you are looking at, and through its built-in TextRazor integration it can run entity extraction, topic classification, or multi-extractor analysis on any column and write the results back in whatever shape you describe. No API mapping, no template config, no script to maintain. You just ask.

Example 1: Entity extraction across a column

For each text in column A, use TextRazor entity extraction and write the extracted entity names and types (person/organization/place) into columns B and C, one cell per row.

SheetXAI calls the TextRazor API for each row, parses the entity array, picks the top entities by relevance score, and writes names into column B and types into column C — one row per source text.

Example 2: IAB classification with confidence scores

Classify each description in column A using the TextRazor IAB taxonomy v3.0 and put the top two categories and confidence scores into columns B, C, D, and E.

The pattern: you describe the output structure you want, not the API fields you need to map. SheetXAI handles the conditional thinking inline — including rows where no high-confidence category is returned.

Try It

Get the 7-day free trial of SheetXAI and open any Google Sheet with a column of text you need analyzed, then ask it to run TextRazor entity extraction or classification on it. The TextRazor integration is included in every SheetXAI plan.