SocialRails
Runs locally · no signup · batch mode

Free Social Media Sentiment Analysis Tool

Paste a post, a whole thread, or a CSV of comments. Get per-row sentiment, aggregate breakdown, and a CSV you can share with the team. Free, no signup.

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How this analyzer works

A transparent lexicon-based analyzer: it matches sentiment and emotion words, adjusts for negation ("not happy"), intensifiers ("very happy"), and diminishers ("slightly happy"). It runs entirely in your browser — your text never leaves the page. It does not reliably catch sarcasm or deeply contextual takes; treat the output as a fast sanity check, not a verdict.

Results

Start typing — results update live.

How the sentiment analyzer works

1

Paste text or CSV

One post for a quick check, or hundreds of comments for a read on a campaign.

2

Lexicon matching

Each word is scored against curated positive, negative, and emotion dictionaries.

3

Context adjustments

Negation, intensifiers, and diminishers are applied before summing.

4

Score + export

Per-row sentiment and confidence, plus an aggregate breakdown and CSV/JSON export.

What teams use this for

Sentiment analysis is most useful when you have volume — a launch, a review drop, a campaign, a new release. Here are the common cases.

Launch and campaign reads

Drop the first 200–500 comments from a launch post in and see the mix before you read each one. Decide what to reply to first.

Customer feedback triage

Paste reviews or support ticket bodies. Sort by score — the strongly negative ones are where your product team should start.

Brand monitoring

Run a daily batch of mentions from Instagram, Facebook, X, or LinkedIn and chart the positive/negative split over time.

Competitor analysis

Paste reviews or posts about competitors. Aggregate sentiment tells you where they are strong and where they are weak.

Content editing

Paste your own draft post. If the tone is more negative than you intended, adjust before publishing.

Classroom and research

Transparent lexicon makes it easy to explain to students — the matched keywords are shown, not hidden behind a black box.

What this tool will (and will not) do

What it does well

  • • Fast triage of high-volume comment threads.
  • • Honest handling of "not happy" / "very bad" / "slightly annoyed".
  • • Per-row scores plus an aggregate you can paste into a report.
  • • Full privacy — your text stays on your device.

What it won't do

  • • Reliably detect sarcasm or deep irony — no free tool does.
  • • Understand culture-specific idioms it was not trained on.
  • • Replace a paid NLP API if you need enterprise-grade accuracy on ambiguous text.
  • • Track mentions automatically — you still need to gather the comments.

Frequently asked questions

Is this social media sentiment analysis tool really free?
Yes. There is no signup, no email wall, no watermark, and no per-post limit. The tool runs entirely in your browser, so you can paste one comment or 500 comments without anything leaving your device.
How does the sentiment analysis work?
This is a transparent lexicon-based analyzer — not a neural model. It matches sentiment and emotion words against curated dictionaries, then adjusts for negation ("not happy"), intensifiers ("very happy"), and diminishers ("slightly happy"). It returns a signed score from −1 to 1, a confidence percentage based on match density, and the specific keywords it matched so you can verify the result.
How accurate is it compared to paid tools?
Lexicon-based analyzers handle clear, straightforward text very well and are fast, private, and fully auditable. They struggle with sarcasm, irony, cultural references, and deeply contextual sentences — and no free tool on the market reliably catches those either. If you need enterprise-grade accuracy on highly ambiguous text, plug your data into a paid NLP API. For 90% of social-media use cases, this is enough.
Can I analyze multiple posts at once?
Yes. Switch to Batch mode, paste one post per line, or upload a CSV (first column = text). You get per-row scores, an aggregate breakdown, and a CSV/JSON export of the results.
Does the tool work for Instagram, Facebook, X, and LinkedIn comments?
Yes. The analyzer treats any text the same way — it does not know which platform the comment came from. Copy comments out of Instagram, Facebook, X, LinkedIn, YouTube, or TikTok and paste them in. Emojis are stripped before matching.
Does it send my data anywhere?
No. All analysis, matching, aggregation, and exporting happens in your browser. Your comments are never uploaded, logged, or sent to a server — which means this tool is safe to use on internal feedback, unreleased brand sentiment, customer DMs, and anything else you would not want to hand to a third-party API.
What does the "confidence" number mean?
Confidence is computed from how much of the text was actually sentiment-bearing words (match density) plus how many words were matched overall. A long post with no emotional words will have low confidence even if the score is 0; a short post with strong matched words can have higher confidence. It is a rough quality-of-signal indicator, not a probability.
Why are some emotion bars at 0%?
Emotion bars only light up when specific emotion words are found — joy words, anger words, sadness words, etc. If your text is emotionally neutral or uses indirect language, the emotion breakdown will show zeros. That is the analyzer being honest rather than inventing scores.

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