The Scenario
You're the community manager at a gaming company. Every Monday morning you open the sheet with the past week's user forum posts — around 400 rows in column A — and you're supposed to have it cleaned up and analyzed before the 10 AM product sync. Sentiment, topics, abuse flags, named entities. Four columns of output that didn't exist when you opened the file.
The bad version:
- Export the sheet, write a Python script (or find the one from six months ago that may or may not still work with the current API version), run it row by row, watch it crash on row 212 because one post is in Japanese and the script only handles English
- Copy the JSON output back into the sheet manually, figure out which nested field maps to which column, realize you pasted entities into the topics column
- Hit the API rate limit at row 300, wait, re-run from there, reconcile the two partial outputs
The product sync is in 45 minutes. You're supposed to be in this meeting surfacing insights, not debugging a data pipeline you inherited from a contractor who left eight months ago.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the data and talks to Tisane for you — no script, no JSON parsing, no column mapping by hand.
Analyze every text in column A using Tisane, then write the detected sentiment into column B, extracted topics into column C, abuse flags into column D, and named entities into column E
What You Get
- Column B: sentiment label per row (positive, negative, neutral) from Tisane's analysis
- Column C: comma-separated topic tags extracted from the post content
- Column D: abuse flag if Tisane detected hate speech, profanity, or cyberbullying — blank if clean
- Column E: named entities mentioned (game titles, character names, usernames) extracted from the text
- Rows where Tisane finds nothing to report in a given category get a blank cell, not an error or a zero
What If the Data Is Not Quite Ready
The posts column has mixed HTML and plain text
Some forum posts in column A still have <br> tags and anchor elements from the CMS export.
Before running Tisane analysis on column A, strip any HTML markup from each cell, then analyze the cleaned text and write sentiment into column B, topics into column C, abuse flags into column D, and entities into column E
Some rows are empty or contain only whitespace
Analyze the text in column A with Tisane for sentiment, topics, abuse, and entities — skip any row where column A is blank or contains only spaces — write results into columns B through E
Posts are split across two tabs: 'This Week' and 'Last Week'
Pull all posts from the 'This Week' tab column A and the 'Last Week' tab column A, run Tisane analysis on each, and write sentiment, topics, abuse, and entity results back into columns B–E on the respective tabs
Full pipeline: clean, analyze, and flag high-severity rows in one shot
Strip HTML from column A, run Tisane analysis on each cleaned post, write sentiment to column B, topics to column C, abuse type to column D, entities to column E, then highlight any row in column D that contains 'hate speech' or 'cyberbullying' in red
One prompt carries the cleanup and the analysis together. No intermediate steps, no intermediate exports.
Try It
Get the 7-day free trial of SheetXAI and open any Google Sheet with a column of user-generated text, then ask it to run Tisane analysis across the whole thing. For a different NLP task, see how to detect language per row or classify content by abuse type. The full list of Tisane workflows is in the hub overview.