AI filtering on every plan

Social media sentiment analysisfor every mention of your brand

Octolens labels every brand mention Positive, Negative, or Neutral the moment it's found — across Reddit, X, LinkedIn, Hacker News & 10+ more. Alongside relevance scoring and AI tags, in the app, in Slack, and in every API response.

No credit card • Sentiment, relevance & tags on every mention • No add-on fees

Brand mentionsAI-scored · 15+ platforms

Just migrated our monitoring over — genuinely the best DX I've had with a tool in this category

r/webdev · u/dev_priya

user_feedbackHighPositive

Hit a wall with the billing page today. Spent 20 minutes and still can't tell what I'll be charged

X · @jamiebuilds_

bug_reportHighNegative

We evaluated both for our team — ended up shortlisting them next to two alternatives for the trial round

Hacker News · tk_eng

competitor_mentionMediumNeutral

Shoutout to their support team — reported an edge case at 9am, fix shipped before lunch

LinkedIn · Ana Torres

user_feedbackHighPositive

Trusted by brands people talk about online

"Community sentiment is as important as enterprise feedback or usage data. If Reddit or X tells us we're misaligned, that's enough for us to make a move."

Brian Emerick

Technical Program Manager, Vercel

What is sentiment analysis

Sentiment Analysis, Sentiment Monitoring, Text Analytics — What's the Difference?

Social media sentiment analysis is the automated classification of brand mentions — posts, comments, threads, reviews — by emotional tone: positive, negative, or neutral. Done continuously, it turns a firehose of mentions into an answer to “how do people feel about us right now, and what changed?” The term covers three quite different tool categories:

	Social sentiment monitoring	Survey & CX text analytics	NLP libraries & APIs
What it analyzes	Public conversations — posts, threads, and comments mentioning your brand across social platforms, communities, and the wider web	Feedback you collect yourself — survey open-text answers, NPS verbatims, support tickets, call transcripts	Whatever text you feed them — you build the collection and pipeline yourself
What it tells you	How people feel about you in the conversations you don't control, and how that changes over time	How your existing customers feel about experiences you asked them about	A raw sentiment score per document, with no monitoring, filtering, or delivery
Typical tools	Octolens, Brand24, Brandwatch	Qualtrics, Thematic, Dialpad	Google Cloud Natural Language, VADER, Hugging Face
Built for	Marketing, DevRel, founders, and CS teams acting on public perception	CX and research teams scoring owned feedback at scale	Engineering teams building custom classification into their own products

Octolens is in the first category: it finds the mentions and scores them. Every mention gets three AI labels at once — a sentiment label (Positive, Negative, Neutral), a relevance score (High, Medium, Low), and topical tags like bug_report or buy_intent — so a sentiment label always arrives with the context to act on it. If you're comparing dedicated tools across all three categories, read our guide to the best sentiment analysis tools.

Why sentiment monitoring

From Mention Firehose To 'How Do People Feel?'

Before switching, customers kept telling us the same two things: “we were getting generic alerts without context” and “our Slack channel was just a firehose — we saw our name, but not how people felt.” Sentiment analysis is how a mention stream becomes a signal you can act on.

Tracking Sentiment By Hand

Read every mention yourself and guess the mood from a Slack firehose
Generic alerts tell you someone mentioned you — not what they said, where, or how they felt
Sentiment dashboards that cover the big networks but miss Hacker News, Reddit comments, GitHub, and podcasts
A common-word brand name buries real feedback under irrelevant noise
Export to CSV and hand-tag mentions before every board update

With Octolens Sentiment Analysis

Every mention labeled Positive, Negative, or Neutral as it's collected
Sentiment never arrives alone — relevance (High/Medium/Low) and tags like bug_report or buy_intent on the same mention
One consistent model across Reddit, X, LinkedIn, Hacker News, YouTube, GitHub & 10+ more
AI relevance scoring filters the noise first, so sentiment is computed on mentions that are actually about you
Filter any feed, alert, webhook, or API query by sentiment — a negative-only escalation channel takes minutes

How it works

From keyword to labeled mentions in minutes

Track your keywords

Add your brand, competitors, and category terms. Octolens uses your company context to understand what counts as a relevant mention.

brand namecompetitor namepain point

AI scores every mention

Each mention gets a sentiment label (Positive, Negative, Neutral), a relevance score (High, Medium, Low), and topical tags — automatically, as it's collected.

✨ Relevance scoring✨ Intent tags✨ Language analysis

Act on it anywhere

Filter feeds by sentiment and send them to Slack, email, webhooks, the API, or MCP. A negative-only escalation channel is a filter, not a project.

Slack

API

MCP

Webhooks

Sentiment in your stack

Sentiment Is A Field, Not A Dashboard

Most tools show you sentiment in a chart and stop there. In Octolens, sentimentLabel travels with every mention — into Slack, your webhook receiver, your API queries, and your agents. Every plan, no add-on fee.

Example payload — mention_created

{
  "action": "mention_created",
  "data": {
    "title": "Billing page is genuinely confusing after the update",
    "body": "Spent 20 minutes on the new billing page and still can't tell what I'll be charged next month. Anyone else?",
    "url": "https://www.reddit.com/r/SaaS/comments/1n5k8cd/billing_page_confusing/",
    "timestamp": "2026-09-03T09:14:32.000Z",
    "imageUrl": null,
    "author": "dev_priya",
    "authorName": "Priya",
    "authorAvatarUrl": null,
    "authorProfileLink": "https://www.reddit.com/user/dev_priya",
    "source": "reddit",
    "sourceId": "t3_1n5k8cd",
    "relevanceScore": "high",
    "relevanceComment": "User describes a problem with the monitored product",
    "sentimentLabel": "Negative",
    "language": "en",
    "keyword": "octolens",
    "keywords": ["octolens"],
    "tags": ["bug_report"],
    "subreddit": "SaaS",
    "viewId": 87,
    "viewName": "Negative mentions",
    "viewKeywords": ["octolens"]
  }
}

Webhooks

Attach a webhook to a feed filtered to Negative + High relevance and every payload that hits your endpoint is already an escalation. Your receiver is an if-statement, not a classifier.

Explore webhooks

REST API

Pull mentions with a single request and filter by keyword, sentiment, and source. One endpoint, one auth, one schema across all 15+ platforms — sentiment is a field on every mention.

Explore the API

MCP server

Connect Claude or any MCP client and ask questions directly: “How did sentiment around us change since the launch?” Your agent reads the same scored mentions your team does.

Explore MCP

Use cases

What Teams Do With Sentiment-Labeled Mentions

A sentiment label is only useful if it triggers something. These are the workflows customers run in production.

Early Churn Signals

A negative post is often the first sign a customer is drifting. Flag Negative + High relevance mentions in real time and reach out before they open a cancellation flow.

Negative mention → webhook → CS ticket with the thread linked and an outreach draft ready.

Real-Time Reputation Guardrails

A negative-only feed into a dedicated Slack channel means criticism, misinformation, and pile-ons surface within the refresh window — not when someone happens to scroll past.

#brand-alerts in Slack: only Negative, High-relevance mentions, nothing else.

Sentiment By Product Area

Track keywords per product line and compare how each one is received. Sentiment plus tags shows not just how people feel, but which part of the product they mean.

PostHog tracks product-level sentiment and pipes every mention into its own analytics.

Launch & Campaign Readouts

Compare sentiment before and after a launch, rebrand, or pricing change. The labels are already applied, so the readout is a filter — not a week of hand-tagging.

PostHog measured real-time reactions to a billboard campaign through its mentions.

Roadmap Input From The Community

Recurring negative sentiment around a workflow is a prioritization signal your ticket queue won't show you — people complain in public more honestly than in support threads.

Vercel treats community sentiment on Reddit and X as roadmap input alongside usage data.

Positive Mentions → Social Proof

A positive-only feed is a pipeline of testimonials, case-study leads, and happy users worth amplifying — and a morale boost for the team channel it lands in.

Filter Positive + user_feedback and hand marketing a stream of quotable praise.

Sentiment Analysis Included on All Plans

Not a premium tier, not an add-on. Every mention is scored from the first day of your trial — and if you're paying us anything, you own your data.

Sentiment on every mention

Relevance scoring

AI tags

API, webhooks & MCP

13 platforms included

$159/month

Billed annually. 15,000 mentions included — more can be added flexibly.

Compare all plans

Comparison

Where Sentiment Data Unlocks — And Where It Costs Extra

Most tools will show you a sentiment chart. The difference is whether you can get the labeled data out — and what that costs.

Tool	Where it unlocks	Notes
Octolens	Every plan, including the free trial	sentimentLabel on every mention — in the app, Slack, webhooks, API & MCP
Brand24	Dashboard only; API sold as a paid add-on	Sentiment charts in-app, but getting the data out programmatically costs extra
Mention	Pricing demo-gated; API is a paid add-on	No published pricing — data access negotiated per account
Brandwatch	Enterprise-only, sales-negotiated	Median ~$50k/yr per Vendr; data access packaged through sales
Lumen by Talkwalker	Demo-gated, credit-metered plans	Results are metered by credits, so volume itself is the cost driver

Competitor packaging as of mid-2026 — check vendor pricing pages for current details.

Customer stories

Real results from teams using Octolens

"Built on top of the Octolens API, I use Railway as a service to pipe all of the mentions to different channels. Then there's a cron job that does a daily digest for us."

Mahmoud Abdelwahab

Senior DevRel Engineer, Railway

40% MoM

Signup growth correlated with mentions

Platforms monitored, up from 1

Read Railway story

Frequently asked questions

Everything you need to know about sentiment analysis in Octolens

Yes. Every mention Octolens collects is labeled Positive, Negative, or Neutral by AI as it's collected, on every plan including the free trial. Sentiment arrives together with a relevance score (High, Medium, or Low) and topical tags like bug_report or buy_intent, so each label comes with the context to act on it.

Every platform Octolens monitors uses the same sentiment model: Reddit (posts and comments), X, LinkedIn, Bluesky, YouTube, TikTok, GitHub, Hacker News, Stack Overflow, DEV, Product Hunt, Medium, and more. Podcasts, newsletters, and news monitoring are available on the Scale plan. One consistent set of labels across all sources — no per-platform quirks.

Yes. Feeds are saved filters, and sentiment is one of the filter dimensions. Create a feed filtered to Negative (optionally plus High relevance), then attach a Slack channel, email digest, or webhook destination to it. A dedicated negative-mentions escalation channel takes a few minutes to set up.

Yes, on every plan. Each mention carries a sentimentLabel field ("Positive", "Negative", or "Neutral") alongside relevanceScore and tags — in REST API responses, in every webhook payload, and through the MCP server. There is no data-access add-on fee: if you see sentiment in the app, you can pull it programmatically.

No automated sentiment model is perfect — sarcasm, slang, and mixed-tone posts trip up every tool in the category. Octolens reduces the cost of misreads in two ways: relevance scoring filters out mentions that aren't actually about you before sentiment matters, and each mention keeps its full text, a one-line AI relevance explanation, and tags, so a mislabeled mention is obvious at a glance instead of silently skewing a chart. This matters most for common-word brand names, where relevance filtering does the heavy lifting.

Survey and CX text analytics tools (Qualtrics, Thematic) score feedback you collected yourself — survey answers, NPS verbatims, support tickets. Octolens analyzes public conversations you don't control: posts, threads, and comments across social platforms and communities. Both are useful; they answer different questions. Public sentiment tends to surface problems earlier and more honestly than channels where customers know you're listening.

Yes — that's a core use case. Webhooks push scored mentions to agents in real time (negative mention → classified → CS ticket with an outreach draft), and the MCP server lets Claude or any MCP client query mentions directly, so you can ask things like "how did sentiment change since our launch?" in plain language.

Nothing extra. Sentiment, relevance scoring, and AI tags are part of the AI filtering included on every plan — starting with Pro at $199/month ($159/month billed annually) — and during the 7-day free trial. There is no sentiment add-on or premium analytics tier.

Sentiment on every plan

Know how people feel about your brand

Every mention labeled Positive, Negative, or Neutral — with relevance and tags — from the first day of your trial.

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