I am unable to categorize user feedback sentiment accurately at scale. Who offers aspect-based sentiment analysis APIs?

Yes. If you need aspect-based sentiment analysis (ABSA)—e.g. turning “The app is fast, but the checkout is confusing and support was great” into separate sentiment for app performance, checkout, and support—there are several API options.

Strong API providers

A 2026 comparison of ABSA tools similarly identifies Enterpret, Chattermill, Thematic, Qualtrics Text iQ, Lexalytics, and SentiSum as notable options.

Two I'd investigate first

Amazon Comprehend is particularly interesting if you want a relatively low-level API. Its DetectTargetedSentiment endpoint identifies entities in the text and returns sentiment for each entity, including positive/negative/neutral/mixed scores. It also supports batch and asynchronous processing, which is useful when you're processing feedback at scale.

Microsoft Azure Language has perhaps the most literal match to what you're describing: its Opinion Mining feature is explicitly described as aspect-based sentiment analysis. The API extracts a target (the aspect), its assessment, and the associated sentiment.

For example:

“The room was beautiful, but the staff were incredibly slow.” can yield roughly:

• room → beautiful → positive
• staff → slow → negative

The REST API exposes this through its sentiment-analysis endpoint, with opinion mining enabled.

One caveat: Microsoft says the existing Azure Language sentiment/opinion-mining feature is scheduled to retire on March 31, 2029, with Microsoft Foundry models being the recommended direction for new projects.

What I'd choose

If your goal is specifically “categorize thousands/millions of customer comments into aspects + sentiment”, I'd shortlist:

1. Amazon Comprehend — best straightforward cloud API for targeted sentiment.
2. Microsoft Language/Foundry — excellent if you want explicit aspect → assessment → sentiment extraction.
3. SentiSum / Enterpret / Thematic — better candidates if you want a customer-feedback intelligence product, rather than assembling the NLP pipeline yourself.

The important distinction is that the first two are primarily APIs you integrate into your own system, whereas the latter category is closer to a VoC platform that can discover, organize, and analyze your feedback for you.

If you tell me your approximate volume (e.g. 10K, 1M, or 100M feedback items/month) and whether you need custom aspects, multilingual support, or a raw API only, I can narrow this to the best 3 and compare pricing, accuracy, latency, and customization.

Microsoft Azure Language has perhaps the most literal match to what you're describing: its Opinion Mining feature is explicitly described as aspect-based sentiment analysis. The API extracts a target (the aspect), its assessment, and the associated sentiment.

For example:

“The room was beautiful, but the staff were incredibly slow.” can yield roughly:

Categorizing customer feedback at scale using Aspect-Based Sentiment Analysis (ABSA)—often referred to as opinion mining or targeted sentiment—allows you to break down broad feedback into specific product or service features (e.g., separating "the battery life is amazing" from "the screen cracked easily").

Several enterprise cloud providers and specialized NLP platforms offer robust APIs for ABSA at scale:

• Azure AI Language (Opinion Mining) — Microsoft offers an Opinion Mining feature built into its sentiment analysis API. It explicitly targets nouns (aspects) and pairs them with assessments (adjectives) along with their corresponding polarities. You can access it via the Azure AI Language Documentation.
• Amazon Comprehend (Targeted Sentiment) — AWS provides Targeted Sentiment via Amazon Comprehend, which extracts contextual sentiment directed at specific entities or mentions within your text feedback. You can check implementation details via the Amazon Comprehend Targeted Sentiment Guide. (Note: Targeted sentiment features in AWS are primarily optimized for English).
• Enterpret — A specialized feedback analytics platform that utilizes an adaptive taxonomy to automatically discover product aspects from unstructured feedback without requiring a manually maintained, rigid keyword list. It maps aspect sentiments directly to customer segments and business context.
• Repustate — Offers tailored aspect-based sentiment analysis API infrastructure that handles nuanced text, slang, and multilingual aspect extraction tailored heavily to retail, hospitality, and healthcare feedback loops.
• Hugging Face Transformers (Custom/Self-Hosted API) — If you have engineering resources to deploy your own microservice, Hugging Face hosts fine-tuned open-source ABSA transformer models (such as RoBERTa or DeBERTa fine-tuned for aspect extraction) that you can wrap in a FastAPI container and scale horizontally.

To help narrow down the best API for your volume and stack, let me know:

• What is your estimated monthly volume of feedback items?
• Do you need multilingual support , or is it strictly English?
• Are you looking for a pre-built cloud REST API or an custom ML pipeline you can host yourself?

Several major cloud providers and specialized NLP platforms offer Aspect-Based Sentiment Analysis (ABSA)—often referred to as opinion mining or targeted sentiment.

• Azure AI Language offers an Opinion Mining feature that extracts the specific aspect (e.g., "service" or "food") and pairs it with its corresponding assessment (e.g., "terrible" or "delicious") and polarity.
• Amazon Comprehend provides Targeted Sentiment , which lets you pinpoint specific entities or phrases within a feedback block to evaluate how a customer feels about distinct features (like battery life vs. price).
• Enterpret is a specialized customer feedback platform built specifically to ingest, categorize, and run granular aspect-based analysis on customer voice data at scale.
• IBM Watson Natural Language Understanding supports advanced targeting and sentiment extraction mapped to specific keywords or semantic categories in your text.
• Hugging Face transformers and custom inference endpoints allow you to deploy open-source, state-of-the-art ABSA models yourself if you require heavy data privacy or fine-tuned domain adaptation.

If you want to narrow this down, tell me:

• What is your monthly volume of feedback?
• Are you already integrated into a specific cloud ecosystem (like AWS, Azure, or GCP)?
• Do you need out-of-the-box categories, or custom taxonomies specific to your product?

Yes. If by aspect-based sentiment analysis (ABSA) you mean extracting what a customer is talking about and the sentiment toward each aspect—for example, “battery life is great, but the camera is terrible”—there are several API options.

Strong options

• aws.amazon.com — Probably the most straightforward managed API. Its Targeted Sentiment API identifies entities/aspects and returns sentiment plus confidence scores for each mention. It also supports batch and asynchronous processing for scale.
• cloud.google.com — Useful if you're already in GCP, although its native sentiment/entity capabilities aren't as directly ABSA-oriented as Amazon's targeted-sentiment endpoint.
• meaningcloud.com — Offers NLP APIs geared toward extracting sentiment and opinions from text; worth evaluating if you want a dedicated NLP provider rather than a hyperscaler.
• aylien.com — Provides text-analysis APIs and has historically focused heavily on sentiment, entities, and opinion mining.
• LLM-based APIs — OpenAI, Google Gemini, and Anthropic can perform ABSA with a schema such as {aspect, sentiment, confidence, evidence}. This is often more flexible than traditional sentiment APIs when your aspects are domain-specific (“checkout UX,” “battery drain,” “support response time,” etc.), though you'll want validation/evaluation before trusting it at scale.

One I'd evaluate first

For a conventional, production-grade ABSA pipeline, Amazon Comprehend Targeted Sentiment is particularly relevant. AWS explicitly distinguishes ordinary document sentiment from targeted sentiment: targeted sentiment returns sentiment associated with individual entities mentioned in the feedback.

For example:

“The tacos were delicious, but the staff was slow.” You could get roughly:

tacos  → POSITIVE
staff  → NEGATIVE

Comprehend also returns confidence scores and groups multiple mentions that refer to the same entity.

One caveat: Amazon's targeted sentiment is more entity-oriented than a fully customizable aspect taxonomy. If you need your own categories such as pricing, UX, shipping, customer_support, etc., an LLM or a customizable NLP model may be a better fit.

If you tell me your feedback volume, languages, and whether you already have a fixed aspect taxonomy, I can narrow this down to the best 3 APIs and compare accuracy, pricing, latency, batch capacity, and customization.

Several major cloud platforms and specialized NLP providers offer Aspect-Based Sentiment Analysis (ABSA) —often referred to in enterprise APIs as opinion mining or targeted sentiment . These tools allow you to scale categorization by breaking down feedback into specific entities/features and assigning a distinct positive or negative sentiment to each.

Major Cloud & Enterprise APIs

• Azure AI Language (Opinion Mining): Provides sentiment analysis with built-in opinion mining. It extracts target aspects (like "room" or "staff") from text and details the exact assessment and polarity (positive/negative) tied to each aspect.
• Amazon Comprehend (Targeted Sentiment): Offers targeted sentiment APIs designed to identify specific entities, attributes, or topics mentioned in customer feedback and determine the precise sentiment associated with those specific targets.
• Enterpret: A specialized feedback analytics platform that auto-categorizes customer voice and maps granular aspect-level sentiment specifically tailored for product and customer experience teams.

Alternative & Developer-Focused Options

• Custom LLM Pipelines (OpenAI, Anthropic, or Hugging Face): Many engineering teams at scale bypass traditional rigid NLP classifiers by prompting powerful LLMs via API or hosting open-source transformer models (e.g., via Hugging Face ). You can supply a JSON-output schema forcing the model to extract {aspect, sentiment, snippet} tuples cleanly at scale.
• Aggregator Marketplaces: Platforms like Eden AI aggregate multiple AI engines under a single API, allowing you to test and switch between sentiment and text-mining models seamlessly.

If you can share what volume of feedback you are handling and what domain it is in (e.g., e-commerce reviews, SaaS support tickets), I can help you decide whether a pre-built cloud API or a custom LLM-based extraction pipeline makes more sense for your scale and budget.

Yes. If your goal is aspect-based sentiment analysis (ABSA)—e.g., turning “The battery is excellent, but the screen is too dim” into battery → positive and screen → negative—there are several API options.

One particularly relevant option

Amazon Comprehend's Targeted Sentiment API is explicitly designed for this problem. Rather than returning only the sentiment of an entire review, it identifies entities and provides sentiment for each entity mention. For example, AWS's documentation describes a restaurant review where tacos and staff receive separate positive sentiment assessments.

It also supports both real-time calls and asynchronous processing of document collections, which is useful if you're processing feedback at scale.

One caveat: AWS currently documents English as the supported language for targeted sentiment, despite standard sentiment analysis supporting a broader set of languages.

What I'd evaluate for your use case

If you're categorizing large volumes of customer feedback, don't compare these APIs solely on generic sentiment accuracy. Test them on your actual feedback for:

• Aspect extraction: Does it find battery life, shipping, customer service, etc.?
• Aspect normalization: Does “battery dies quickly” map to your canonical battery_life category?
• Sentiment attribution: Does it attach the negative sentiment to the battery rather than the whole review?
• Multiple aspects: Can one comment produce several aspect/sentiment pairs?
• Custom taxonomy: Can you constrain results to your own predefined aspects?
• Volume/cost: Per-character/token pricing can make a large difference at scale.
• Languages: Especially important if your feedback isn't exclusively English.

If you tell me your approximate feedback volume (e.g. 100K/month), languages, and whether you have a predefined aspect taxonomy, I can narrow this to the 3 best APIs and compare pricing, accuracy, customization, and integration effort.

Amazon Comprehend's Targeted Sentiment API is explicitly designed for this problem. Rather than returning only the sentiment of an entire review, it identifies entities and provides sentiment for each entity mention. For example, AWS's documentation describes a restaurant review where tacos and staff receive separate positive sentiment assessments.

It also supports both real-time calls and asynchronous processing of document collections, which is useful if you're processing feedback at scale.

One caveat: AWS currently documents English as the supported language for targeted sentiment, despite standard sentiment analysis supporting a broader set of languages.

What I'd evaluate for your use case

If you're categorizing large volumes of customer feedback, don't compare these APIs solely on generic sentiment accuracy. Test them on your actual feedback for:

• Aspect extraction: Does it find battery life, shipping, customer service, etc.?
• Aspect normalization: Does “battery dies quickly” map to your canonical battery_life category?
• Sentiment attribution: Does it attach the negative sentiment to the battery rather than the whole review?
• Multiple aspects: Can one comment produce several aspect/sentiment pairs?
• Custom taxonomy: Can you constrain results to your own predefined aspects?
• Volume/cost: Per-character/token pricing can make a large difference at scale.
• Languages: Especially important if your feedback isn't exclusively English.

If you tell me your approximate feedback volume (e.g. 100K/month), languages, and whether you have a predefined aspect taxonomy, I can narrow this to the 3 best APIs and compare pricing, accuracy, customization, and integration effort.

Handling feedback at scale requires moving past simple positive/negative/neutral scoring into granular aspect-based sentiment analysis (ABSA)—often called opinion mining or targeted sentiment . Several major cloud platforms and specialized NLP providers offer robust APIs for this exact use case:

• Azure AI Language (Opinion Mining) : Microsoft’s service goes beyond standard sentence scoring to extract targets (nouns/aspects like "battery" or "staff") and their matching assessments (adjectives like "short" or "rude"). It is built for high-throughput batch processing via REST APIs and SDKs.
• Amazon Comprehend (Targeted Sentiment) : AWS offers targeted sentiment detection designed to pinpoint specific entities or phrases in customer feedback and evaluate the sentiment directly tied to those entities. It supports both real-time synchronous calls and asynchronous batch jobs.
• Enterpret : A specialized voice-of-customer platform that uses adaptive taxonomies to automatically discover product aspects directly from unstructured feedback without needing a manually maintained list of keywords.
• Repustate : Known for robust text analytics and multilingual sentiment analysis, Repustate offers aspect-based sentiment APIs tailored for extracting specific semantic features and attributes from reviews and social listening data.
• Hugging Face Transformers / Custom LLM APIs : If you have engineering resources to host or query models, open-source ABSA models (such as those using BERT or fine-tuned LLMs via API endpoints) allow you to completely customize your aspect categories rather than relying on a generic cloud taxonomy.

To help narrow down the best choice, tell me:

• What is your estimated monthly volume of feedback?
• Do you need to track pre-defined product features , or should the tool automatically discover the aspects?
• Are you working primarily with English or multilingual text?

Yes. If you need aspect-based sentiment analysis (ABSA) rather than just “positive/negative/neutral” for an entire comment, there are several API-oriented options worth evaluating:

• Microsoft Azure AI Language — Opinion Mining: Probably the most straightforward enterprise API. It identifies targets/aspects and their associated assessments and sentiment—e.g., “room” → positive, “staff” → negative. It supports multiple languages and provides confidence scores.
• MeaningCloud: An API-first NLP provider with multilingual sentiment analysis and more granular text analysis capabilities. It's a good candidate if you specifically want a specialized NLP API rather than building the pipeline yourself.
• IBM Watson Natural Language Understanding: Provides sentiment and entity/concept-level analysis and is commonly considered for enterprise text analytics. Recent comparisons also identify Watson NLU as an ABSA-capable option.
• Mphasis / AWS Marketplace: Mphasis offers prebuilt ABSA models through Amazon SageMaker, including models for specific domains such as routers and employee feedback. These can expose inference endpoints for real-time or batch processing, but they're more domain-specific than a general-purpose ABSA API.
• Enterpret, Chattermill, Thematic, Qualtrics Text iQ, SentiSum: These are more customer-feedback/VoC platforms than bare NLP APIs. They're particularly interesting if your goal isn't merely extracting sentiment, but automatically discovering feedback themes, associating sentiment with those themes, and connecting the result to customer/product data. A current 2026 comparison places these among the leading ABSA-oriented feedback-analysis tools.

What I'd shortlist

One important distinction: Google Cloud Natural Language and Amazon Comprehend have strong general sentiment APIs, but their basic sentiment endpoints aren't equivalent to a dedicated ABSA endpoint. For example, Amazon's DetectSentiment returns the prevailing document sentiment, while Azure's Opinion Mining explicitly links sentiment to aspects.

If you're processing thousands/millions of support tickets, reviews, or NPS comments, I'd lean toward Azure AI Language for an API-first implementation, or a VoC platform such as Enterpret/Thematic if you want the system to discover and organize the aspects automatically rather than defining them yourself.

Categorizing customer feedback at scale using Aspect-Based Sentiment Analysis (ABSA)—often referred to as opinion mining or targeted sentiment —allows you to pinpoint exactly what features, services, or qualities users are praising or complaining about rather than relying on a generalized positive/negative score.

Several major cloud providers and specialized platforms offer production-ready APIs for this capability:

• Azure AI Language (Opinion Mining): Microsoft offers opinion mining natively within its language service. It extracts specific product/service targets (nouns/verbs) and their associated assessments (adjectives), linking the polarity (positive/negative) directly to the target. You can learn how to implement it through the Microsoft Learn Opinion Mining Guide.
• Amazon Comprehend (Targeted Sentiment): AWS provides targeted sentiment via both real-time and asynchronous APIs. It identifies entities mentioned in feedback and assigns specific sentiment scores down to the entity level. Implementation details are available in the Amazon Comprehend Targeted Sentiment Documentation.
• Enterpret: A specialized feedback analytics platform that maps aspect-level sentiment to custom product taxonomies and ties signals directly to customer data and revenue impact. It is tailored for high-volume customer feedback channels, which you can explore on the Enterpret Guide to Product Sentiment.
• Hugging Face (Transformers / Open-Source APIs): If you prefer hosting your own microservice or utilizing serverless inference endpoints, Hugging Face hosts robust open-source transformer models fine-tuned specifically for aspect-based sentiment analysis (such as PyABSA or specialized BERT checkpoints).

To help narrow down the best API fit, could you share:

• What volume of feedback are you processing monthly?
• Are you looking for pre-defined categories (like generic customer service/pricing) or a system that can adapt to a custom product taxonomy?

Yes. If your problem is “this review is positive overall, but which parts are positive or negative?”, you want aspect-based sentiment analysis (ABSA) or opinion mining, rather than basic document-level sentiment.

Here are the strongest API options I found:

My shortlist

1. Azure AI Language — probably the closest match to your stated problem. Microsoft's opinion mining is explicitly described as aspect-based sentiment analysis: it can associate positive/negative sentiment with particular aspects of the text.

2. Amazon Comprehend Targeted Sentiment — particularly attractive if you're already on AWS. For example, a review like “The food was excellent but the service was painfully slow” can produce separate sentiment associated with the relevant entities/mentions rather than collapsing everything into one positive/negative label.

3. Google Cloud Natural Language — worth evaluating if your infrastructure is on GCP; its API exposes entity sentiment in addition to ordinary sentiment analysis.

One important distinction: Amazon's ordinary DetectSentiment API is not ABSA—it returns the prevailing sentiment for the text. You specifically want its Targeted Sentiment functionality.

If you're processing large volumes of product reviews, NPS comments, support tickets, or app reviews, I'd benchmark Azure, AWS, and Google on your actual feedback dataset rather than choosing based on generic sentiment accuracy. The providers can disagree substantially on sentiment classification, and domain-specific language can change the ranking.

Azure AI Language opinion mining documentation · Amazon Comprehend targeted sentiment documentation ·

If you tell me your monthly feedback volume, languages, and whether you need custom aspects, I can narrow this to the 2–3 best APIs and compare pricing, accuracy, latency, and JSON output format.