Glossary

Comment Data Glossary

Plain-language definitions of the terms behind comment exporting and social analytics. Each entry links to a deeper guide.

AI Comment Analysis: Using a language model to summarize, cluster, and tag thousands of comments — themes, sentiment, questions, complaints — far faster than reading them by hand. Turns a raw export into an insights report.
Audience Analysis: Profiling who is actually in a comment section — language, location signals, and interests — to understand a creator's or campaign's real audience. Useful for verifying a media kit against reality.
Brand Mention Monitoring: Tracking where and how often a brand or product is named in comments across creators' posts. Exporting comments and filtering the text column catches mentions the platform's native tools miss.
Brand Safety: Making sure a creator's content and comment section won't expose your brand to controversy, hate, or spam before you partner with them. A quick comment export surfaces the tone of their community.
Bulk Export: Exporting comments from many posts or creators at once into a single dataset, instead of one post at a time. Essential for agency-scale audits and multi-creator campaign reporting.
Comment Export: Pulling the comments from a social post into a downloadable file (CSV, Excel, or JSON) so they can be filtered, counted, and analyzed in a spreadsheet. Platforms offer no native export, so a tool reads the public comment thread and writes one row per comment.
Comment Finder: A workflow for locating comments by a specific username across posts. Since platforms have no cross-post comment search, a finder exports the relevant posts' comments and filters the author column.
Comment Picker: A tool that selects random winners from a post's comments for giveaways, optionally de-duplicating entries and requiring conditions like tags or follows. Fair, repeatable draws instead of scrolling by hand.
Comment Scraping: The automated collection of comment data from public posts at scale. "Scraping" and "exporting" describe the same result; scraping emphasizes the automated crawling, exporting emphasizes the spreadsheet you get out.
Competitor Analysis: Studying a competitor's posts and comment sections to learn what their audience loves, complains about, and asks for. Their comment threads are unfiltered market research you can export in minutes.
CSV / Excel Export: A comma-separated file with one comment per row and columns for author, text, likes, timestamp and more — the format spreadsheets read directly. Open it as UTF-8 so emoji and non-Latin text stay intact.
Engagement Rate: The share of an audience that actively interacts with a post — likes, comments, shares — relative to views or followers. Comment-level engagement (likes and replies per comment) reveals which comments actually resonated, not just the post-level average.
Fake Comment / Bot Detection: Spotting comments left by bots or engagement farms — repetitive text, generic emoji, burst timing, empty profiles. Exported data with timestamps and author fields makes these patterns visible.
Influencer Vetting: Checking whether an influencer's audience and engagement are genuine before a paid partnership. Reading real comments exposes bot activity, off-topic audiences, and how a creator talks to their followers.
KOL (Key Opinion Leader): An influencer whose audience trusts their recommendations in a niche. Comment data helps shortlist KOLs by showing how their audience actually reacts, not just follower counts.
KOS (Key Opinion Seller): A creator who drives direct sales, not just awareness — the sales-focused counterpart to a KOL, central to TikTok Shop and Southeast Asian social commerce. Purchase-intent signals in comments help identify them.
Live Comments: Comments posted during a live stream, which vanish when the broadcast ends. Capturing them requires exporting during or right after the stream, since there's no permanent thread to revisit.
Media Kit Verification: Cross-checking the audience an influencer claims in their media kit against the real demographics visible in their comments. Protects against inflated or mismatched audience claims before you pay.
Product Research: Validating whether a product has real demand by reading what an audience already asks for in comments. Recurring "I need this" and "does it come in…" comments are demand signals before you commit inventory.
Purchase Intent: Signals in a comment that someone is ready to buy — "where can I get this", price questions, "link please". Filtering comments for purchase intent turns a comment section into a warm lead list.
Sentiment Analysis: Classifying each comment as positive, negative, or neutral to measure how an audience feels about a video, product, or brand. Done at scale it turns a comment section into a percentage breakdown you can track over time.
Social Listening: Monitoring what people say about a brand, product, or topic across social platforms to surface trends, complaints, and opportunities. Exported comments are the raw material — real audience language instead of survey summaries.
Social Selling: Turning social interactions — especially comment replies — into sales relationships. Speed matters: replying to buying-intent comments within the first day meaningfully lifts conversion.
TikTok Shop: TikTok's in-app commerce layer where creators sell products directly. Comments on Shop videos carry buyer signals — sizing questions, restock requests, price objections — that guide inventory and content.
UGC (User-Generated Content): Content created by an audience rather than the brand — comments, reviews, reaction videos. Comment sections are a dense, free source of UGC and campaign ideas, especially the phrasing customers use to describe a product.
Word Cloud: A visual of the most frequent words in a comment set, sized by how often they appear. A fast way to see the dominant themes, slang, and product names in a thread before deeper analysis.