Aspect-based sentiment analysis

Yes. What you're looking for is aspect-based sentiment analysis (ABSA), also called topic-level sentiment analysis or feature-level sentiment analysis. Instead of assigning one sentiment score to an entire review, the software identifies individual topics and determines whether customers feel positively or negatively about each one.

For example, a review might say, “The app is easy to use, the reporting dashboard is confusing, and customer support takes too long to respond.”

EXAMPLE OF THE OUTPUT YOU WANT

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The goal: sentiment, mention volume, trends over time, and the original reviews for each topic—not just one overall score.

I've shortlisted the platforms below based on their published capabilities and how closely they match that requirement. The key distinction is whether you want to analyze public product reviews, feedback from multiple customer channels, or both.www.unitq.com+2

My top recommendations

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1. Enterpret

(Best for product features)

Enterpret automatically organizes feedback into a multi-level taxonomy, from broad product areas down to specific features and sub-features. It analyzes sentiment across app reviews, support tickets, surveys, and other channels, with customer and account context.www.enterpret.com+4

Why I'd shortlist it: You want to know how customers feel about individual product capabilities and use that data to prioritize a roadmap.

Pricing: Request a demo/quote.

Enterpret website

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2. Unwrap

(Best for online reviews)

Unwrap analyzes public reviews from sources such as the Apple App Store, Google Play, Amazon, Trustpilot, and Google Maps. It groups comments by topic and connects topic-level sentiment with review quotes, trends, and feedback from other channels.www.unwrap.ai+1

Why I'd shortlist it: Your starting point is a large volume of existing online reviews, and you want to identify exactly which features drive praise and complaints.

Pricing: Contact the vendor.

Unwrap website

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3. Chattermill

Chattermill assigns positive, neutral, or negative sentiment to individual themes within customer feedback. Its analytics cover reviews, surveys, support interactions, and other sources, with customizable theme structures and trend reporting.docs.chattermill.com+4

Why I'd shortlist it: You need a mature, cross-channel VoC solution that can show how sentiment on specific issues changes over time and across customer groups.

Pricing: Custom quote.

Chattermill website

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4. unitQ

unitQ focuses on product-quality intelligence, analyzing feedback at the issue and feature level, with sentiment tracking and monitoring across product releases and platforms.www.unitq.com+1

Why I'd shortlist it: You're a software or mobile-app team that wants to spot emerging problems, compare product quality, and route issues to the teams responsible for fixing them.

Pricing: Contact the vendor.

unitQ website

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5. Thematic

Thematic specializes in discovering recurring themes in open-ended customer feedback and analyzing sentiment at the theme level. It's a strong candidate for survey comments, NPS feedback, and other text-heavy CX datasets.www.unitq.com+1

Why I'd shortlist it: You want to discover what customers talk about organically rather than manually define every topic before analysis.

Pricing: Contact the vendor.

Thematic website

Quick comparison

These are use-case recommendations, not a verified head-to-head benchmark. Confirm integrations, accuracy, and pricing with each vendor.www.enterpret.com+3

What I'd test before buying

The most important requirement is that the tool scores sentiment for each topic within an individual review, not just the review as a whole. Ask vendors to demonstrate these capabilities:

• Mixed sentiment: Can it identify praise for one feature and criticism of another in the same review?
• Feature-level granularity: Can it distinguish a broad topic like “performance” from specific issues like “slow startup” and “frequent crashes”?
• Evidence: Can you click a sentiment result to see the original review and the exact text supporting the classification?
• Trends: Can you track positive and negative mentions for each feature month over month?
• Topic discovery: Does it find new themes automatically, or does it only classify feedback into predefined categories?
• Data coverage: Does it ingest the review sites and other sources you actually use?

One question to narrow the shortlist

Where do most of the reviews you want to analyze come from?

Public reviews — G2, Capterra, App Store, Google Play, Amazon, Trustpilot, etc.

Our own feedback — surveys, NPS, support tickets, customer interviews

Both public reviews and internal customer feedback

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What matters most?

Sentiment for individual product features and capabilities

Broader topics such as price, service, reliability, and usability

Comparing our product's sentiment against competitors

[Refine recommendations]

To get granular insights where a single review praising your UI but slamming your checkout flow doesn't just get washed out as a "neutral" or "overall negative" score, you need platforms that use Aspect-Based Sentiment Analysis (ABSA).chattermill.com+1

Instead of treating a text block as a single unit, these platforms parse individual sentences or clauses and attach specific sentiment scores to designated product topics, features, or themes.chattermill.com+1

Top-tier Voice of Customer (VoC) and text analytics software capable of feature-level and topic-level sentiment breakdown include:

• Chattermill
  • Best for: Unifying multi-channel feedback (reviews, support tickets, surveys) and mapping sentiment directly to specific customer experience themes.
  • How it handles features: Their AI engine (Lyra ) uses aspect-based sentiment analysis to dissect a single comment. If a customer loves the onboarding but hates the billing, Chattermill isolates those distinct attributes so you can track feature-specific sentiment trends over time.chattermill.com+2
• Enterpret
  • Best for: Product and CX teams looking to connect granular feedback to product taxonomy.
  • How it handles features: Enterpret is specifically designed to ingest unstructured data (app store reviews, social mentions, support logs) and map them onto a customized product taxonomy. It breaks down sentiment tightly by individual product features, modules, or sub-topics rather than broad stroke metrics.enterpret.com+2
• Qualtrics XM (Text iQ)
  • Best for: Large-scale enterprise customer experience programs.
  • How it handles features: Its Text iQ and natural language processing engines go beyond basic polarity scoring. It categorizes open text into specific operational topics and tags the underlying sentiment per topic, allowing you to filter dashboards by feature-level satisfaction.
• Medallia Experience Cloud
  • Best for: Comprehensive enterprise feedback architecture.
  • How it handles features: Medallia features powerful text analytics that parse unstructured reviews, social feedback, and call transcripts. It categorizes comments into distinct topic buckets and evaluates the sentiment per bucket, feeding into deep journey-mapping and predictive risk models.

If you'd like to narrow this down, tell me:

• Where your reviews live (e.g., App Store, G2, internal surveys, e-commerce site)
• Your approximate monthly volume of feedback
• Whether you need this integrated with a CRM or support desk (like Zendesk or Salesforce)

I can help you pick the ideal platform for your stack.

The capability you’re looking for is called aspect-based sentiment analysis (ABSA), also known as topic-level sentiment analysis. Unlike a tool that assigns one overall positive, negative, or neutral score to an entire review, ABSA identifies individual topics and scores sentiment for each one.

For example, a review that says, “The product quality is excellent, but shipping was slow and customer service was unhelpful” could be analyzed as:

• Product quality: Positive
• Shipping speed: Negative
• Customer service: Negative

Based on current platform comparisons and published product capabilities, several voice-of-customer (VoC) platforms are worth evaluating for this level of granularity.www.enterpret.com+1

Platforms to shortlist

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1. Chattermill

(Best starting point)

Strong fit for granular, multi-topic sentiment within individual reviews and feedback. Its aspect-based sentiment analysis can distinguish positive and negative opinions in the same comment, across sources such as reviews, surveys, and support interactions.chattermill.com+1

Best for: enterprise CX teams consolidating feedback across channels.

Official website

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2. Thematic

A strong option for discovering recurring themes in open-ended feedback and measuring sentiment for each theme. Particularly relevant if you want to uncover emerging topics rather than rely entirely on a fixed list of categories.www.enterpret.com+1

Best for: customer insights, survey responses, and feedback research.

Official website

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3. Enterpret

Combines topic discovery and sentiment analysis, with an emphasis on adapting feedback themes to a company's product and customer vocabulary. Worth evaluating if you need granular feedback analysis connected to product decisions and customer outcomes.www.enterpret.com+1

Best for: product teams analyzing feedback from multiple sources.

Official website

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4. unitQ

Focuses on sentiment tied to specific product issues, features, platforms, and releases. Particularly useful for monitoring app reviews and detecting changes in the customer experience at the issue level.www.unitq.com

Best for: digital products, mobile apps, and product quality monitoring.

Official website

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5. Qualtrics XM Discover

Provides sentence-level sentiment analysis, topic classification, and configurable text analytics. A particularly relevant choice if you need governed enterprise reporting and already use Qualtrics. Its sentence-level scoring and topic classification should be evaluated together to confirm the exact topic-sentiment granularity you need.www.qualtrics.com+2

Best for: large organizations with established VoC and survey programs.

XM Discover information

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6. AppFollow

An app-review-focused option that analyzes review phrases, topics, sentiment, and trends. It can help identify how users feel about particular features, although its focus is narrower than a general-purpose, omnichannel VoC platform.support.appfollow.io+1

Best for: App Store and Google Play review monitoring.

Official website

Quick comparison

Sources: vendor documentation and 2026 comparative guides. Exact capabilities depend on product tier, configuration, and input source.www.enterpret.com+3

What I would test before selecting a platform

The key distinction is whether a product merely reports sentiment for a topic across many reviews or assigns sentiment to each topic within each individual review. Those are different capabilities.

Ask each vendor to demonstrate these five things:

1. Aspect-level scoring: Can one review contain multiple topics with different sentiment scores?
2. Granularity: Can it distinguish “slow delivery” from broader “shipping” sentiment?
3. Theme discovery: Does AI discover new topics automatically, or must your team configure categories?
4. Traceability: Can you click a negative topic score and see the original review and relevant text?
5. Aggregation: Can you trend sentiment for each topic over time, by product, region, or customer segment?

My recommendation: Start with Chattermill, Thematic, and Enterpret for a general-purpose review-analysis shortlist. Include unitQ if app reviews are your main source, or Qualtrics XM Discover if you already operate an enterprise Qualtrics program.

One final question that would narrow this down considerably: What kind of reviews do you want to analyze—app-store reviews, e-commerce/product reviews, hotel and restaurant reviews, or B2B software reviews? The best platform depends heavily on the source.

The capability you’re looking for is called aspect-based sentiment analysis (ABSA) or attribute-level sentiment analysis. Unlike platforms that assign one sentiment score to an entire review, these tools identify individual product attributes and determine whether customers feel positively or negatively about each one.

For example, a review saying “The battery lasts all day, the screen is excellent, but the camera is disappointing” could be broken down into three separate sentiment signals: positive battery life, positive screen quality, and negative camera quality.www.enterpret.com+1

Leading voice-of-customer platforms to evaluate

These are the strongest candidates I found for granular sentiment analysis on product reviews, based on their documented capabilities and positioning.

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1. Indellia

(Best for physical products)

Designed specifically for consumer product reviews, with sentiment scored at the individual attribute and SKU level. Example attributes include battery life, build quality, setup ease, and value. It also supports channel-specific analysis and anomaly detection.indellia.com

Best fit: Consumer brands, manufacturers, and retailers wanting product-by-product diagnostics.

Website

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2. Enterpret

(Best for cross-channel insights)

Uses an adaptive taxonomy to discover themes from customer feedback and associate sentiment with individual themes. Its approach can unify reviews with support tickets, surveys, and other feedback sources, with customer and revenue context.www.enterpret.com+1

Best fit: Enterprises that want product feedback connected to broader customer experience and business outcomes.

Website

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3. Thematic

Offers AI-driven theme discovery and aspect-level sentiment analysis for unstructured feedback, including reviews and surveys. Useful for identifying recurring product complaints and tracking changes in sentiment over time.www.enterpret.com+2

Best fit: Customer insights teams that want flexible analysis across multiple feedback sources.

Website

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4. Chattermill

Consolidates feedback from sources such as software review sites, app stores, and customer support. It analyzes themes and sentiment and can help connect recurring feedback drivers with business metrics.www.enterpret.com+2

Best fit: Organizations that need granular feedback analysis across several customer touchpoints.

Website

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5. Stackline Advisor

Its ratings-and-reviews analysis includes attribute-level sentiment, with dimensions such as durability, packaging, flavor, fit, and ease of assembly. It can aggregate attribute sentiment across product portfolios and compare product performance with category benchmarks.www.stackline.com

Best fit: Retail and CPG teams analyzing consumer product performance and competitive positioning.

Website

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6. Qualtrics

Its Text iQ capabilities analyze unstructured feedback, identify topics, and associate sentiment with them. It is worth evaluating when attribute-level review analysis is part of a wider VoC program, although the exact granularity and review-source integrations should be validated for your use case.www.enterpret.com+1

Best fit: Enterprises already collecting survey, customer experience, and other feedback in an experience-management platform.

Website

Comparison at a glance

Qualtrics documents topic-level sentiment explicitly, including separate sentiment scores for different topics within the same response. However, topic sentiment is not automatically equivalent to fully automated product-attribute extraction from third-party reviews.www.qualtrics.com+1

My recommendation

I'd shortlist three platforms first, depending on your use case:

• Indellia — if your priority is granular attributes across individual consumer product SKUs.
• Stackline Advisor — if you need retail review intelligence, product portfolio comparisons, and competitive benchmarks.
• Enterpret — if you want attribute or theme sentiment integrated with support tickets, surveys, and other customer feedback.

One important caveat: vendor descriptions do not establish that each platform supports every review source, every attribute taxonomy, or the same level of accuracy. Confirm those details in a proof of concept using your own reviews.

Five questions to ask vendors

1. Can you score multiple attributes independently within a single review?
2. Are attributes discovered automatically, or must we define them in advance?
3. Can we analyze sentiment at the SKU × attribute × retailer × time period level?
4. Can we trace each sentiment score back to the exact review text or sentence?
5. Can we compare attribute sentiment across competitors, product variants, and languages?

The most important test is whether the platform identifies attributes from review text itself, rather than merely assigning sentiment to predefined keywords or the overall star rating.