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Sentiment Analysis

AI
Overall + per-section sentiment verdict with evidence and shifts.
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What you'll get: an overall sentiment verdict + per-section table with evidence + notable shifts between sections. For another use, try AI · Tone & Style Analyzer (voice + register).

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Private & secure
AI tools process your PDF in memory, send only the extracted text to the model, and discard everything immediately. No file is persisted on our servers.
No watermarks
Clean output, no branding, no daily limits.
Pay only for what you use
Purchased credits never expire. Cancel anytime.

Why people use Sentiment Analysis

Sentiment Analysis reports overall sentiment (positive / negative / neutral / mixed) plus per-section sentiment with evidence and shifts. Useful for analyzing reviews, customer feedback, internal communication tone, and document emotional arc.

Customer review / NPS analysis
Drop a stack of customer feedback (compiled into one PDF), get per-section sentiment with evidence quotes. Faster than reading every review, and the section-by-section breakdown surfaces patterns hand-coding would miss.
Internal email / chat thread tone
When a thread escalates or de-escalates, sentiment-by-section surfaces the inflection points. Useful for HR / management reviewing communication patterns in conflict situations.
Press / coverage sentiment
Industry coverage (analyst reports, press releases, social media archives) analyzed for sentiment toward your company / product. Useful for the “how are we landing?” communication audit.
Document emotional arc
Long-form content (memoirs, narrative non-fiction, reports) mapped for sentiment progression. Useful for editorial review where pacing matters — if sentiment is monotone for 200 pages, the reader experience flatlines.
Survey / interview qualitative coding
Open-ended survey responses or interview transcripts compiled and analyzed. Replaces manual sentiment-coding for first-pass analysis; researchers verify a sample for confidence.

How Sentiment Analysis works

Three steps. Sign in to run, zero retention — files never persisted on our servers.

  1. STEP 1
    Drop the doc
    PDF up to 100 MB. Works on prose — reviews, threads, feedback compilations, narrative content.
  2. STEP 2
    We score + locate evidence
    Server-side extraction, then per-paragraph and per-section sentiment scoring. Evidence quotes pulled for each sentiment verdict. Sentiment shifts (positive → negative inflections) surfaced with the trigger sentence.
  3. STEP 3
    Get a structured report
    Markdown output with: overall verdict / per-section verdicts / evidence quotes / sentiment shifts / trigger phrases. Page citations on every finding.

What makes pdfcraft ai different

  • Citations on every claim
    Every AI summary, answer, and translation links back to the page it came from. You can verify the source before relying on the output. Hallucination is bounded, not blind.
  • Pay only for what you use
    No subscription. Purchased credits never expire. Cancel anytime. Cost shown before you run — exact credit amount appears after upload.
  • Multi-provider routing, no vendor lock-in
    We balance multiple AI providers per call to optimise cost and quality. If one has an outage, the next call automatically tries another. You're not bound to a single vendor's pricing or downtime.
  • Zero retention by design
    Your PDF lives in memory only as long as the AI call needs it, then it's discarded — nothing is persisted to our servers. We don't fine-tune models on your data. Verifiable in your dashboard's audit log.
  • Honest about model limits
    When a doc exceeds the model's context window we surface that the output was truncated (we render result.wasTruncated in every AI tool's UI). When a number can't be verified from the source we flag it. No silent failures.

Frequently asked questions

How accurate is sentiment classification?
Strong on clearly polarized content (reviews, opinions). Weaker on neutral / ambiguous content (technical docs, factual reporting) where sentiment isn’t the doc’s point. Sarcasm / irony are detection challenges — treat the score as directional.
Will it work for non-English content?
Best results in English. Indian-language sentiment (Hindi, Tamil, Bengali) works at lower accuracy — sentiment lexicons are English-trained and translated. For multilingual feedback analysis, run AI Translate first, then sentiment.
Does it understand context-dependent sentiment?
Tries — e.g. “the queue was long but the food was great” surfaces both negative (queue) and positive (food) sentiment with the right targets. Mixed sentiment is reported as such, not flattened to neutral.
Can it identify what’s causing the sentiment?
Surfaces evidence quotes with each sentiment verdict, so you can see WHY the model called it positive / negative. The cause analysis isn’t formal aspect-based sentiment (which would need topic clustering) — for that, pair with Extract Entities for who-is-mentioned.
What’s the credit cost?
3 credits per doc. Cost is fixed regardless of doc size.
What about doc length limits?
Long docs (200+ pages) chunk and process per-chunk. Each chunk gets its own sentiment scoring; the final report aggregates with the section structure preserved.

Need the entities mentioned too?

Extract Entities surfaces people / organizations / places / dates from the same doc. Pairs with sentiment analysis for the “what’s being said about whom” analysis.

Try Extract Entities

Related PDF tools

Other tools in the AI category — built on the same private, in-browser pipeline.

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