AI Sentiment Analysis Tool.
Paste review text or upload a CSV. Get aspect-based sentiment with confidence scoring, citation excerpts, and a polarity distribution — built on deterministic retrieval, not LLM guesswork.
The short answer
An AI sentiment analysis tool scores the emotional polarity of text — positive, neutral, negative — and, in aspect-based mode, breaks that score down by specific features mentioned in the text (battery, design, support). Indellia's tool grounds every score in a retrieved citation, so you see the source passage behind each number. This approach reduces the hallucination risk of pure LLM scoring.
Analyze sentiment.
Paste up to 50 reviews, or upload a CSV with one row per review. Sample report opens in the same window.
Sentiment scoring that shows its work.
Generic sentiment analysis tells you a review is "negative." That's rarely useful. A single review is almost always mixed — positive on one aspect, negative on another. Treating a review as a single polarity score throws away the signal that matters.
Aspect-based sentiment. The tool identifies the aspects mentioned in each review (suction power, mapping, battery life, app, noise, support) and scores polarity per aspect. One review can be +72 on suction and −44 on support. That's the signal a product team acts on.
Confidence per aspect. Each sentiment score comes with a confidence band. Low-confidence scores (where the text is ambiguous or thin) are flagged, so you don't mistake weak signals for strong ones.
Citation excerpts. Every aspect-level score is grounded in the actual passages from the input text. You can see the sentence behind the score — a deterministic-AI approach that differs from pure-LLM scoring where the reasoning is opaque. See the sentiment analysis guide for method detail.
Four steps, any text.
Paste or upload your text.
Up to 50 reviews in the paste box, or a CSV with one row per review. The tool accepts reviews, survey open-ends, social mentions, and support-ticket bodies. Rating columns are respected where present.
Aspects are extracted.
The Theme Agent identifies the aspects mentioned across the input set — automatically, without a pre-built taxonomy. Aspects are typically product features, support touchpoints, or purchase experiences.
Polarity scores compute.
Each aspect gets a polarity score from −100 to +100 with a confidence band. Overall polarity (positive/neutral/negative) is also computed as a distribution. Ambiguous passages are flagged rather than guessed.
You get a sample report.
The output is a one-page report with a polarity ring, an aspect table, citation excerpts, and a methodology sidebar. For continuous scoring across your feedback corpus, start a free trial.
What the output looks like.
A trimmed preview of the aspect table. See the full sample report.
When to use the full Indellia platform.
The free tool runs on a single batch of text. That's enough for a one-off sentiment read — a pre-meeting analysis, a post-launch debrief, or a sentiment snapshot before a campaign.
The Indellia platform runs aspect-based sentiment continuously across every review, ticket, and survey response in your corpus. It tracks aspect-level sentiment per SKU over time — so you can see whether "App" sentiment improved after a firmware release, or whether "Battery life" is declining on a specific Model#.
It grounds every aspect score in retrieved citations (not LLM hallucination) and exposes the whole corpus to Claude, ChatGPT, and Cursor via the Indellia MCP Server. See transparent pricing.
Frequently asked questions
What is an AI sentiment analysis tool?
An AI sentiment analysis tool scores the emotional polarity of text — positive, neutral, negative — and, in aspect-based mode, breaks that score down by feature, service, or experience mentioned. Indellia's tool adds deterministic grounding: every aspect-level score is tied to a retrieved citation from the source text, so you can verify the reasoning.
What's the difference between overall and aspect-based sentiment?
Overall sentiment scores a piece of text as one polarity (e.g., "this review is negative"). Aspect-based sentiment breaks the same text apart by feature or experience (e.g., "positive on design, negative on battery, neutral on support"). Aspect-based is the more useful view for product and CX teams because it surfaces the specific thing customers like or dislike.
How accurate is sentiment scoring on short reviews?
Short reviews (under 20 words) are often ambiguous even to humans. The tool flags low-confidence scores explicitly rather than guessing. In practice, review sets of 50+ produce stable aggregate signal even when individual short reviews are uncertain. The full Indellia platform improves confidence over time by learning from your domain-specific vocabulary.
Does this work on non-English feedback?
The free tool is English-first. The full Indellia platform supports Spanish, French, German, Italian, Portuguese, Dutch, Japanese, and Mandarin at production quality, with additional languages in beta. For multilingual analysis at scale, book a demo.
How is this different from using an LLM directly?
An LLM can score sentiment, but the reasoning is opaque and responses vary run to run. Indellia grounds sentiment in retrieved citations from the source text, producing deterministic, reproducible scores. See the deterministic AI glossary entry for the distinction.
Can I export the sentiment scores?
The free tool returns a one-page report you can save. CSV export of aspect-level scores is available in the Indellia platform at both SME and Mid-Market tiers. Snowflake pushback is available on Mid-Market for direct warehouse integration.
Have a specific question?
Indellia's AI agents answer with citations from real customer feedback across Amazon, Walmart, Best Buy, and 20+ retail channels.
Aspect-based sentiment, continuously, per SKU.
See which aspects are getting better or worse per Model#, per retailer, per month.