Question 1

How does SnitchFeed detect sentiment?

Accepted Answer

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.

Question 2

What sentiment categories does SnitchFeed use?

Accepted Answer

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.

Question 3

Can it handle sarcasm and negation?

Accepted Answer

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.

Question 4

Is the sentiment about my brand or about the post in general?

Accepted Answer

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.

Question 5

Does sentiment analysis work on Reddit?

Accepted Answer

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.

Question 6

Can I track sentiment about my competitors?

Accepted Answer

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.

Question 7

Can I get notified about negative mentions only?

Accepted Answer

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.

Question 8

How is this different from AI relevance scoring?

Accepted Answer

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.

Sentiment analysis that reads the thread, not the word list

Word-counting sentiment tools get the tone wrong

Tagged at capture, alongside relevance and intent

A mention is captured

The AI reads the whole picture

Sentiment is tagged with the rest of the score

Tone routes the mention

Sentiment alone is half a signal

A switching opportunity

A fire worth catching early

A testimonial waiting to happen

A buyer asking the room

A tag your workflow can actually use

Send negative mentions to the team that owns them

Open a frustrated thread differently than a curious one

Spot switching intent before they pick a destination

See how people actually feel about your brand.

One sentiment model, every platform's context

Reddit

X (Twitter)

LinkedIn

Bluesky

What teams use this for

Common questions about sentiment analysis

Stop guessing the tone. Start reading the room.