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Sentiment analysis that reads the thread, not the word list

Every social media mention is tagged positive, negative, or neutral by AI that understands sarcasm, negation, and context, and it arrives paired with relevance and buying intent so you know what to do about it.

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“The ability to use AI sentiment scoring and get insights on data is incredibly useful. Very hard to find at this price point!”

Jenna GensicProduction Manager, Callisto Marketing

Word-counting sentiment tools get the tone wrong

Traditional social media sentiment analysis counts positive and negative words. Real posts negate, joke, compare, and ask questions. Here is how the two approaches read the same four mentions.

  • "Not bad at all. Honestly impressed with the onboarding."

    Keyword tool: Negative

    Flags "not bad" as negative words

    SnitchFeed: Positive

    Negation understood: this is praise

  • "Love spending my entire morning fighting this dashboard."

    Keyword tool: Positive

    Counts "love" as a positive word

    SnitchFeed: Negative

    Sarcasm read from context: this is a complaint

  • "Switched away from Acme last month. Best decision this year."

    Keyword tool: Positive

    Upbeat words, so it scores positive

    SnitchFeed: Negative for Acme

    Churn story: negative for the brand being left

  • "Is Acme any good? Considering it for our support team."

    Keyword tool: Neutral, ignored

    No emotional words, so it gets buried

    SnitchFeed: Neutral + high intent

    A buyer asking for validation: the best kind of mention

Tagged at capture, alongside relevance and intent

Sentiment analysis runs automatically on every mention as part of the AI scoring pass. Nothing to configure, no separate tool to check.

  1. 01

    A mention is captured

    SnitchFeed detects a post or comment matching your keywords across Reddit, LinkedIn, X, or Bluesky. Reddit and Bluesky are ingested in near real time; LinkedIn and X are captured on each scan.

  2. 02

    The AI reads the whole picture

    The model reads the full post, the surrounding thread when available, and your brand context. That is what lets it catch negation, sarcasm, and posts where the sentiment about you differs from the sentiment of the words.

  3. 03

    Sentiment is tagged with the rest of the score

    The mention is stamped positive, negative, or neutral in the same pass that assigns its 0-100 relevance score and buying intent level. Every mention in your feed carries all three.

  4. 04

    Tone routes the mention

    Alerts show the sentiment tag up front, and webhook filters can route by it: negatives to support, positives to marketing, everything to your data warehouse. Your team knows the mood before anyone clicks through.

Sentiment alone is half a signal

A negative mention is a crisis or an opportunity depending on who it is about. That is why SnitchFeed never shows you sentiment in isolation: combined with intent and relevance, the same tag means four very different next moves.

Negative + competitor mention

A switching opportunity

Someone is publicly frustrated with a tool you compete against. They are already motivated to move: they just have not picked a destination yet. These are the highest-converting conversations in social listening.

Negative + your brand

A fire worth catching early

An unhappy customer posting in public is a churn risk and a reputation risk at the same time. Getting to the thread in minutes instead of days usually changes how it ends.

Positive + your brand

A testimonial waiting to happen

Unprompted praise is marketing material you did not have to ask for. Thank them, amplify it, and ask if you can quote them while the enthusiasm is fresh.

Neutral + high buying intent

A buyer asking the room

Posts like "anyone recommend a tool for X?" carry no emotion at all, which is exactly why sentiment-only tools bury them. Paired with intent scoring, they surface at the top of your feed.

A tag your workflow can actually use

Sentiment in SnitchFeed is not a pie chart on a dashboard. It is a field on every mention that your alerts, replies, and integrations react to.

Route by sentiment

Send negative mentions to the team that owns them

Webhook filters support sentiment, so complaints can flow to your support tooling while praise lands in your marketing channel. No manual triage in between.

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Reply in the right tone

Open a frustrated thread differently than a curious one

AI reply drafts see the sentiment tag before writing. A complaint gets acknowledgment first; a recommendation request gets a direct answer. You review and post.

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Watch competitors too

Spot switching intent before they pick a destination

Negative sentiment on a competitor's name is one of the highest-converting signals in social listening. Pair sentiment tagging with a competitor listener and you catch the complaint before they have chosen where to go instead.

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See how people actually feel about your brand.

Set up a listener in under 5 minutes. Every mention arrives tagged with sentiment, relevance, and intent.

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No credit card requiredSentiment tagging on by default

One sentiment model, every platform's context

The same model runs everywhere, but each platform gives it different material to work with. Reddit sentiment analysis is the richest of the set: long threads and candid comments leave little room for ambiguity.

Reddit

Posts and comment threads give the AI the most context of any platform. Long-form complaints, detailed comparisons, and follow-up replies all feed the sentiment call.

X (Twitter)

Short posts lean harder on thread context and account signals. Ambiguous one-liners are tagged neutral rather than guessed at.

LinkedIn

Professional tone hides strong opinions in polite language. The AI reads past the diplomacy to the actual verdict.

Bluesky

Similar dynamics to X: short posts, thread-dependent meaning, with sentiment tagged as part of real-time ingestion.

What teams use this for

Competitor monitoring

Catch negative sentiment about your competitors, the highest-converting signal in social listening.

Lead generation

Neutral-sentiment posts asking for recommendations are buying signals sentiment alone would bury.

Brand monitoring

Know whether a mention of your brand is a complaint, a question, or praise before you open the thread.

Common questions about sentiment analysis

Still have questions? Reach out at hello@snitchfeed.com

Still have questions? Reach out at hello@snitchfeed.com

How does SnitchFeed detect sentiment?
An AI model reads the full post, its surrounding thread when available, and your brand context, then tags the mention positive, negative, or neutral. This happens in the same pass that assigns the relevance score and buying intent level, so every mention arrives with all three dimensions attached. It is not keyword polarity: no word lists, no counting happy and sad words.
What sentiment categories does SnitchFeed use?
Every mention is tagged positive, negative, or neutral. Deliberately simple: three categories you can act on beat a fine-grained emotion taxonomy you would have to interpret. Sentiment is always shown alongside the relevance score and intent level, which is where the nuance actually lives.
Can it handle sarcasm and negation?
Straightforward cases, yes. "Not bad at all" is tagged positive, and obvious sarcasm like "love dealing with this dashboard every day" inside a complaint thread is tagged negative. Subtle irony in very short posts, especially on X, is harder, and the model prefers tagging ambiguous posts neutral over making a wrong call. You can always click through to the original post.
Is the sentiment about my brand or about the post in general?
The AI evaluates sentiment in the context of the brand or keywords your listener tracks. A post saying someone happily switched away from a competitor reads as positive language, but if your listener tracks that competitor, the relevant signal is dissatisfaction with them, and the tags and intent scoring reflect that framing.
Does sentiment analysis work on Reddit?
Yes, and Reddit is where it works best. Threads are long, contextual, and candid, which gives the model far more signal than a short post on X. SnitchFeed monitors both Reddit posts and comments, and mentions are captured and tagged in near real time.
Can I track sentiment about my competitors?
Yes. Set up a listener on competitor names and every mention of them gets the same sentiment, relevance, and intent tagging as mentions of your own brand. Negative competitor mentions paired with switching intent are one of the most valuable feeds in the product.
Can I get notified about negative mentions only?
Webhook filters support sentiment, so you can push only negative mentions to an endpoint, a Slack workflow, or your support tooling. Slack, Discord, and email alerts fire based on each listener's relevance threshold, with the sentiment tag visible on every alert so your team knows the tone before clicking through.
How is this different from AI relevance scoring?
Same pass, different question. Relevance asks whether the post is about your category. Buying intent asks whether the author is in-market. Sentiment asks how they feel. Every mention gets all three, because any one of them alone is easy to misread: a negative post is an opportunity or a crisis depending on whether it is about your competitor or about you.

Stop guessing the tone. Start reading the room.

SnitchFeed tags sentiment on every mention automatically. Setup takes 5 minutes. First results appear in 60 seconds.

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Sentiment tagging on by defaultReddit, LinkedIn, X, BlueskySlack and Discord alertsCancel anytime