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Data Science AnalyticsTop 9 Best Text Sentiment Analysis Software of 2026
Find top 10 text sentiment analysis software to analyze customer feedback, reviews, social media.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
MonkeyLearn
Prebuilt sentiment models combined with custom model training in the same workspace
Built for teams building sentiment scoring with minimal engineering and practical customization.
Lexalytics
Entity-level sentiment output that links polarity scores to extracted concepts
Built for teams deploying multilingual sentiment to operational dashboards and downstream systems.
Google Cloud Natural Language
Magnitude-based sentiment plus label at both document and sentence levels
Built for teams integrating sentiment into production text analytics and enrichment pipelines.
Related reading
Comparison Table
This comparison table evaluates leading text sentiment analysis software used to classify customer feedback, product reviews, and social media posts by sentiment and related signals like emotions and key phrases. It compares tools including MonkeyLearn, Lexalytics, Google Cloud Natural Language, Amazon Comprehend, and Microsoft Azure AI Language across core capabilities so teams can match features to data and deployment needs.
| # | Tool | Category | Overall | Features | Ease of Use | Value |
|---|---|---|---|---|---|---|
| 1 | MonkeyLearn Provides sentiment analysis and text classification for customer feedback and social media using customizable models and batch or API scoring. | API-first | 8.6/10 | 8.8/10 | 8.3/10 | 8.6/10 |
| 2 | Lexalytics Offers sentiment, emotion, and semantic analysis via cloud and on-prem deployments for large-scale text analytics. | enterprise NLP | 8.1/10 | 8.8/10 | 7.2/10 | 8.0/10 |
| 3 | Google Cloud Natural Language Analyzes sentiment and entities in text with managed Natural Language API endpoints for customer feedback and review mining. | managed NLP | 8.1/10 | 8.6/10 | 7.8/10 | 7.6/10 |
| 4 |  Amazon Comprehend Detects sentiment and key phrases in text using managed NLP features designed for review and customer feedback analytics. | managed NLP | 8.1/10 | 8.4/10 | 8.3/10 | 7.5/10 |
| 5 | Microsoft Azure AI Language Performs sentiment analysis on unstructured text using the Azure AI Language services APIs for customer feedback and social text. | managed NLP | 8.2/10 | 8.6/10 | 7.9/10 | 7.9/10 |
| 6 | Hugging Face Inference API Runs sentiment analysis using hosted transformer models with an inference API and fine-tuning support for domain-specific customer text. | model hub | 8.0/10 | 8.3/10 | 8.0/10 | 7.7/10 |
| 7 | ParallelDots Offers sentiment analysis through cloud APIs and web services that classify positive, negative, and neutral sentiment for text. | API | 8.1/10 | 8.5/10 | 8.0/10 | 7.8/10 |
| 8 | RapidAPI Sentiment Analysis Provides access to multiple sentiment analysis endpoints from different providers via a unified API marketplace for text processing. | API marketplace | 7.4/10 | 7.6/10 | 7.2/10 | 7.2/10 |
| 9 | Atlas.ti Enables qualitative text analysis with coding and sentiment-adjacent features for structured and systematic review interpretation. | qualitative analytics | 7.2/10 | 7.6/10 | 6.9/10 | 6.9/10 |
Provides sentiment analysis and text classification for customer feedback and social media using customizable models and batch or API scoring.
Offers sentiment, emotion, and semantic analysis via cloud and on-prem deployments for large-scale text analytics.
Analyzes sentiment and entities in text with managed Natural Language API endpoints for customer feedback and review mining.
Detects sentiment and key phrases in text using managed NLP features designed for review and customer feedback analytics.
Performs sentiment analysis on unstructured text using the Azure AI Language services APIs for customer feedback and social text.
Runs sentiment analysis using hosted transformer models with an inference API and fine-tuning support for domain-specific customer text.
Offers sentiment analysis through cloud APIs and web services that classify positive, negative, and neutral sentiment for text.
Provides access to multiple sentiment analysis endpoints from different providers via a unified API marketplace for text processing.
Enables qualitative text analysis with coding and sentiment-adjacent features for structured and systematic review interpretation.
MonkeyLearn
API-firstProvides sentiment analysis and text classification for customer feedback and social media using customizable models and batch or API scoring.
Prebuilt sentiment models combined with custom model training in the same workspace
MonkeyLearn stands out with a library of prebuilt sentiment and text classification models plus an interface to build custom models without heavy engineering. It supports running sentiment scoring on batches of text and extracting structured signals from unstructured inputs through trained models. The platform also emphasizes workflow and automation by integrating model outputs into downstream processes via connectors and APIs.
Pros
- Prebuilt sentiment and classification models reduce setup time for common use cases
- Custom model training supports domain-specific sentiment labels and categories
- API access enables embedding sentiment scoring into applications and pipelines
- Batch processing and dataset tools streamline large-scale labeling and evaluation
- Visual workflow and results views make model output easier to audit
Cons
- Model quality depends heavily on labeled training data coverage
- Complex multi-stage text pipelines require careful orchestration and validation
- Interpretability is limited to model outputs rather than fine-grained token reasoning
Best For
Teams building sentiment scoring with minimal engineering and practical customization
More related reading
Lexalytics
enterprise NLPOffers sentiment, emotion, and semantic analysis via cloud and on-prem deployments for large-scale text analytics.
Entity-level sentiment output that links polarity scores to extracted concepts
Lexalytics stands out for production-focused text analytics that combine sentiment with linguistic processing like normalization, tokenization, and classification. The platform supports multilingual analysis and provides sentiment scoring at document and feature levels, which helps teams connect sentiment to specific entities and topics. It also offers flexible model configuration and integration paths for embedding sentiment into existing workflows. Lexalytics is geared toward operational sentiment use cases where governance and repeatable scoring matter more than lightweight experimentation.
Pros
- Multilingual sentiment scoring with consistent linguistic preprocessing
- Configurable sentiment models that support domain-tuned behavior
- Entity and topic level sentiment enables targeted reporting
- Enterprise integration options fit existing analytics workflows
Cons
- Workflow setup and model configuration require more effort than simple APIs
- Fine-grained tuning can be complex without clear operational playbooks
- Output interpretation depends on understanding the scoring and feature logic
Best For
Teams deploying multilingual sentiment to operational dashboards and downstream systems
Google Cloud Natural Language
managed NLPAnalyzes sentiment and entities in text with managed Natural Language API endpoints for customer feedback and review mining.
Magnitude-based sentiment plus label at both document and sentence levels
Google Cloud Natural Language stands out for combining sentiment analysis with broader NLP features under a single managed API set. Sentiment analysis supports document-level and sentence-level scoring, including overall sentiment magnitude and label. The service also provides entity, syntax, and classification capabilities that can enrich downstream text analytics pipelines.
Pros
- Sentence and document sentiment scores with magnitude plus labeled polarity
- Managed NLP stack also includes entities and syntax for richer analytics
- Strong integration path via Google Cloud SDKs and service account authentication
Cons
- Scoring granularity is fixed to sentence and document levels, not custom spans
- Custom domain tuning requires additional ML work outside the sentiment API
- Complex workflows need multiple API calls to cover entities and sentiment together
Best For
Teams integrating sentiment into production text analytics and enrichment pipelines
More related reading
Amazon Comprehend
managed NLPDetects sentiment and key phrases in text using managed NLP features designed for review and customer feedback analytics.
Sentiment detection providing both sentiment labels and confidence scores
Amazon Comprehend stands out for making sentiment analysis a managed, serverless text processing capability inside AWS. It can detect sentiment labels and provide sentiment scores for documents and record-level text, and it supports both batch processing and real-time inference via API. It also integrates cleanly with other AWS services for data ingestion, orchestration, and downstream analytics.
Pros
- Managed sentiment analysis with label output and confidence scores
- Supports both batch sentiment detection and real-time inference APIs
- Strong AWS integration for pipelines with S3, EventBridge, and data processing
Cons
- Sentiment granularity is limited to overall document or record sentiment
- Accuracy can drop on short, sarcastic, or domain-specific phrasing
- AWS-centric workflow can add overhead for non-AWS data stacks
Best For
AWS-first teams needing scalable sentiment detection with minimal ML engineering
Microsoft Azure AI Language
managed NLPPerforms sentiment analysis on unstructured text using the Azure AI Language services APIs for customer feedback and social text.
Sentiment analysis API in Azure AI Language returns per-text structured sentiment results
Microsoft Azure AI Language stands out for pairing sentiment analysis with the broader Azure AI Language suite for text analytics and language enrichment. It supports sentiment scoring through prebuilt capabilities that return structured results for each text input. Users can integrate outputs into larger Azure workflows using SDKs, REST endpoints, and common Azure services for routing, storage, and orchestration.
Pros
- Structured sentiment outputs integrate cleanly with Azure pipelines and storage
- Consistent API and SDK access supports batch and real-time scoring patterns
- Works alongside related language features for fuller text analytics workflows
Cons
- Building an end-to-end pipeline still requires Azure service configuration
- Model behavior can be opaque without careful evaluation on domain-specific text
- Latency and throughput need design work for high-volume real-time scoring
Best For
Teams building Azure-native sentiment analysis into broader text analytics workflows
More related reading
Hugging Face Inference API
model hubRuns sentiment analysis using hosted transformer models with an inference API and fine-tuning support for domain-specific customer text.
Model hub-backed inference that routes requests to different sentiment-capable Hugging Face models
Hugging Face Inference API stands out by serving pretrained NLP models directly through a single inference endpoint, which reduces setup work for sentiment analysis. It supports text sentiment workloads by running transformer models for classification tasks and by exposing outputs as structured inference results. Teams can switch models quickly by targeting different Hugging Face model IDs while keeping the same request flow. Model choice and output formatting are the main levers for tailoring sentiment behavior without building ML pipelines.
Pros
- Direct inference for transformer sentiment models without building an ML stack
- Simple model selection through model IDs for rapid sentiment iteration
- Consistent API responses that fit common classification result handling
- Works well for both single requests and batched text workloads
Cons
- Sentiment quality depends heavily on selecting the right hosted model
- Limited control over pre-processing and tokenization steps versus custom pipelines
- Throughput and latency can vary across hosted models and hardware backends
- No built-in dataset labeling, evaluation, or model training workflow for sentiment
Best For
Teams deploying transformer sentiment analysis via API with minimal ML engineering
ParallelDots
APIOffers sentiment analysis through cloud APIs and web services that classify positive, negative, and neutral sentiment for text.
Sentiment analysis API that outputs polarity labels with machine-readable sentiment scores
ParallelDots stands out with ready-to-use sentiment APIs focused on English text and fast scoring workflows. It delivers polarity labels and confidence-oriented outputs for analyzing customer feedback, reviews, and social posts at scale. The product also supports language-related natural language processing building blocks that pair with sentiment for practical analytics pipelines.
Pros
- Sentiment API returns polarity plus score signals suitable for automation
- Strong performance oriented toward short-form texts like reviews and comments
- Simple integration path for embedding sentiment into existing systems
- Provides model outputs that work well for dashboards and alerts
Cons
- Coverage is best for English and can limit multilingual sentiment needs
- Less emphasis on interactive labeling workflows than analytics-first tools
- Limited customization options for domain-specific training
Best For
Teams integrating sentiment scoring into applications and review analytics pipelines
More related reading
RapidAPI Sentiment Analysis
API marketplaceProvides access to multiple sentiment analysis endpoints from different providers via a unified API marketplace for text processing.
Sentiment scoring returned via API endpoints with both labels and numeric scores
RapidAPI Sentiment Analysis stands out by delivering sentiment scoring through the RapidAPI marketplace rather than a single purpose-built UI. It provides API-based text sentiment analysis endpoints that return sentiment labels and scores for supplied text. The main workflow is orchestration of an external model via API calls, which suits systems that already process text in bulk. Integration focus is stronger than native reporting, because the core deliverable is programmatic sentiment output.
Pros
- Marketplace access to multiple sentiment APIs under one developer workflow
- API responses provide sentiment labels and numeric scores for downstream rules
- Works well for batch processing inside existing text pipelines
Cons
- Limited built-in analytics and dashboards compared with dedicated sentiment platforms
- Quality depends on the selected underlying model provider behind the API
- No turnkey training or domain adaptation tools for custom sentiment targets
Best For
Teams integrating sentiment scoring into applications without heavy UI requirements
Atlas.ti
qualitative analyticsEnables qualitative text analysis with coding and sentiment-adjacent features for structured and systematic review interpretation.
Linking sentiment-coded segments to Atlas.ti coding and memos inside one project
Atlas.ti distinguishes itself with an interpretation-led workflow that links qualitative coding to automated language processing. It supports sentiment analysis through text import, code and memo structures, and repeatable analysis views that help trace meaning back to source passages. The platform also fits teams that need document-level organization, collaboration, and audit trails rather than sentiment dashboards alone.
Pros
- Qualitative coding stays linked to sentiment-coded text segments for traceable interpretation
- Robust project organization supports multi-document sentiment studies and iterative analysis
- Team collaboration tools and memos help reconcile sentiment with researcher reasoning
Cons
- Sentiment outputs integrate more like codes than standalone analytics dashboards
- Workflow setup takes time for teams without qualitative analysis experience
- Automation value depends on consistent text preparation and coding conventions
Best For
Research teams running interpretive sentiment analysis with code-based traceability
Conclusion
After evaluating 9 data science analytics, MonkeyLearn stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Text Sentiment Analysis Software
This buyer's guide covers how to select text sentiment analysis software for customer feedback, reviews, and social media signals. It compares options including MonkeyLearn, Lexalytics, Google Cloud Natural Language, Amazon Comprehend, Microsoft Azure AI Language, Hugging Face Inference API, ParallelDots, RapidAPI Sentiment Analysis, Atlas.ti, and related endpoints. The guide focuses on concrete capabilities like sentiment granularity, multilingual support, entity-level scoring, workflow integration, and interpretability for audit and decision-making.
What Is Text Sentiment Analysis Software?
Text sentiment analysis software classifies the emotional polarity and intensity expressed in text such as reviews, tickets, survey comments, and social posts. It turns unstructured text into structured outputs like sentiment labels, confidence scores, and magnitude values that downstream systems can route and report on. Teams use these systems to prioritize issues, summarize customer experience, and monitor trends across topics and entities. Tools like Amazon Comprehend and Google Cloud Natural Language provide managed sentiment endpoints, while MonkeyLearn supports customizable models and workflow automation via API and connectors.
Key Features to Look For
The right sentiment tool depends on the outputs needed by the workflow, the level of granularity required, and the operational effort available.
Prebuilt sentiment models plus custom model training in one workspace
MonkeyLearn combines prebuilt sentiment and text classification models with custom model training so teams can move from baseline accuracy to domain-specific sentiment labels without changing platforms. This reduces setup time for common sentiment use cases while still supporting customization for industry vocabulary.
Entity-level sentiment tied to extracted concepts
Lexalytics links polarity scores to extracted entities and topics so sentiment can be reported by product features, services, or named concepts instead of only by whole documents. This entity-level output supports targeted reporting for operational dashboards and decision workflows.
Magnitude-based sentiment with document and sentence scoring
Google Cloud Natural Language provides both document-level and sentence-level sentiment that includes a label and a magnitude value. This supports workflows that need intensity tracking across sentences, not just a single overall rating per text item.
Confidence scores with managed, scalable sentiment detection
Amazon Comprehend returns sentiment labels along with confidence scores so downstream rules can filter low-confidence results. It also supports batch processing and real-time inference via API for scalable review and customer feedback analytics.
Azure-native sentiment outputs for pipeline integration
Microsoft Azure AI Language returns structured sentiment results per text input and fits into Azure workflows using SDKs, REST endpoints, and Azure services for storage and orchestration. This suits teams that want sentiment scoring embedded alongside other Azure text analytics steps.
Model selection by model ID via hosted transformer inference
Hugging Face Inference API routes requests to different sentiment-capable transformer models using Hugging Face model IDs with the same request flow. This enables fast sentiment iteration across model choices without building dataset labeling and training workflows.
How to Choose the Right Text Sentiment Analysis Software
A practical selection process starts by matching sentiment granularity and enrichment requirements to the specific outputs each platform delivers.
Define the sentiment granularity needed for the business decision
Choose sentence-level sentiment if decisions depend on localized statements, since Google Cloud Natural Language provides sentiment scoring at both sentence and document levels with magnitude and labels. Choose document- or record-level sentiment if the workflow only needs an overall polarity signal, since Amazon Comprehend and other managed services return overall sentiment at higher levels rather than custom span-level annotations.
Decide whether sentiment must attach to entities and topics
Pick Lexalytics when sentiment must connect to extracted concepts because it outputs entity and topic-level sentiment that links polarity to the underlying extracted units. Use this to build dashboards that highlight which entities drive negative sentiment instead of only reporting negative overall feedback trends.
Match the integration style to the existing engineering workflow
Use MonkeyLearn when sentiment needs to plug into downstream pipelines with a workflow and automation approach that includes APIs plus connectors for embedding model outputs into processes. Choose RapidAPI Sentiment Analysis when the goal is to orchestrate sentiment scoring across multiple provider endpoints under a unified developer workflow.
Plan for language coverage and normalization behavior
Select Lexalytics for multilingual sentiment scoring paired with consistent linguistic preprocessing like normalization and tokenization. Choose solutions like ParallelDots when the sentiment use case focuses primarily on English reviews and comments with fast scoring and straightforward polarity outputs.
Align the tool to whether customization is required or optional
Choose MonkeyLearn for teams that want to train custom sentiment models for domain-specific labels and categories inside the same workspace. Use Hugging Face Inference API or Google Cloud Natural Language for teams that can start with managed or hosted transformer behavior and only switch models rather than build full labeling and training workflows.
Who Needs Text Sentiment Analysis Software?
Text sentiment analysis software fits teams that need automated polarity signals for operational reporting, customer experience workflows, or interpretive qualitative analysis.
Teams building sentiment scoring with minimal engineering and practical customization
MonkeyLearn fits this need because it delivers prebuilt sentiment models plus custom model training in the same workspace, which supports domain-specific sentiment labels. It also supports batch processing and API access for embedding sentiment scoring into applications and pipelines.
Teams deploying multilingual sentiment to operational dashboards and downstream systems
Lexalytics fits because it provides multilingual sentiment scoring and links sentiment to entities and topics for targeted reporting. The combination of entity-level sentiment and configurable models supports operational dashboards that connect polarity to extracted concepts.
Teams integrating sentiment into production text analytics and enrichment pipelines
Google Cloud Natural Language fits because it provides sentence and document sentiment with magnitude and labels along with entities and syntax for richer enrichment. Amazon Comprehend fits AWS-first pipelines because it offers managed sentiment with labels and confidence scores via batch and real-time APIs.
Research teams running interpretive sentiment analysis with traceability
Atlas.ti fits because it links sentiment-coded text segments to coding and memos inside projects for traceable interpretation. The workflow supports organization and collaboration for multi-document sentiment studies where researcher reasoning must remain attached to source passages.
Common Mistakes to Avoid
Selection mistakes often happen when teams pick a sentiment tool that cannot produce the granularity, enrichment, or workflow fit needed by the target system.
Assuming sentiment quality will transfer without labeled domain coverage
MonkeyLearn can require labeled training data coverage because model quality depends heavily on how well labeled examples cover the domain. Hugging Face Inference API can also produce variable quality depending on the selected hosted model, so model choice without evaluation can lead to inconsistent sentiment behavior.
Choosing a tool that only returns overall polarity when sentence-level insight is required
Amazon Comprehend sentiment granularity is limited to overall document or record sentiment, which can miss intensity changes across sentences. Google Cloud Natural Language provides both sentence and document sentiment with magnitude and labels, which matches workflows that require localized sentiment tracking.
Ignoring the need for entity-level sentiment when reports must explain what is driving negativity
Tools that focus on overall sentiment can limit root-cause analysis when the workflow requires entity or topic attribution. Lexalytics provides entity-level sentiment output that links polarity scores to extracted concepts for targeted reporting.
Overbuilding multi-stage pipelines without validation and orchestration checks
MonkeyLearn can require careful orchestration for complex multi-stage text pipelines because interpretability is primarily through model outputs rather than token-level reasoning. RapidAPI Sentiment Analysis can also hide provider differences since quality depends on the selected underlying model provider behind the API.
How We Selected and Ranked These Tools
We evaluated each tool on three sub-dimensions with weights of 0.4 for features, 0.3 for ease of use, and 0.3 for value. The overall rating equals 0.40 × features + 0.30 × ease of use + 0.30 × value. MonkeyLearn separated from lower-ranked options by combining prebuilt sentiment models with custom model training in the same workspace, which scored strongly in the features dimension because it supports both fast deployment and domain-specific sentiment outputs. That same setup also improved ease-of-use scoring because teams can move from baseline sentiment scoring to custom sentiment labels without changing platforms or rebuilding pipelines.
Frequently Asked Questions About Text Sentiment Analysis Software
Which tool is best for building custom sentiment models without heavy ML engineering?
MonkeyLearn fits teams that want prebuilt sentiment models and custom model training in the same workspace without setting up a full ML pipeline. Hugging Face Inference API can also swap sentiment models quickly via model IDs, but it focuses on serving pretrained models through a single endpoint rather than training in a user interface.
Which platforms provide both document-level and sentence-level sentiment outputs?
Google Cloud Natural Language returns sentiment at both document and sentence levels, including magnitude and label. Amazon Comprehend and Microsoft Azure AI Language also support per-text structured sentiment results, and Amazon Comprehend includes confidence scores alongside sentiment labels.
What’s the best choice for multilingual sentiment analysis tied to entities and topics?
Lexalytics is built for multilingual deployments and can score sentiment at the document and feature levels, which helps associate polarity with extracted concepts. Google Cloud Natural Language supports entities and syntax features that can enrich downstream sentiment pipelines, while Lexalytics emphasizes entity-level sentiment output tied to specific extracted items.
Which option is strongest for production deployments inside major cloud ecosystems?
Amazon Comprehend is a serverless sentiment capability designed for AWS-first pipelines and provides labels plus confidence for batch and real-time inference. Microsoft Azure AI Language offers the same operational fit for Azure workflows, while Google Cloud Natural Language consolidates sentiment with other managed NLP functions under one API set.
Which tools deliver sentiment integrated into workflows via APIs and connectors?
MonkeyLearn integrates model outputs into downstream processes through connectors and APIs, which supports end-to-end workflow automation. RapidAPI Sentiment Analysis also centers on API-based sentiment scoring endpoints, making it suitable when an existing system already handles text orchestration.
How do developers choose between hosted transformers and platform-specific sentiment models?
Hugging Face Inference API supports transformer-based sentiment classification by running through a single inference endpoint and switching behavior by selecting different model IDs. MonkeyLearn provides a mix of prebuilt sentiment models and custom model training, which is useful when model behavior must be tailored beyond swapping pretrained models.
Which software is best for customer feedback, reviews, and social posts at high throughput?
ParallelDots is positioned for fast sentiment scoring on English text and outputs polarity labels plus machine-readable sentiment scores for review and social workloads. RapidAPI Sentiment Analysis similarly supports bulk programmatic sentiment scoring, but the core value sits in external API endpoints rather than a dedicated reporting workflow.
Which platform is better for explainable qualitative analysis with audit trails instead of dashboards?
Atlas.ti supports an interpretation-led workflow where sentiment analysis is connected to coding, memos, and repeatable views that trace meaning back to source passages. This approach suits research and governance needs when stakeholders require traceability rather than aggregated sentiment charts alone.
What output differences typically matter when integrating sentiment into analytics models or databases?
Google Cloud Natural Language includes magnitude and label at document and sentence levels, which is useful for downstream numeric scoring and feature engineering. Amazon Comprehend provides sentiment labels and confidence scores, while RapidAPI Sentiment Analysis and ParallelDots return polarity labels with numeric sentiment-oriented outputs that can be mapped directly into structured tables.
Tools reviewed
aws.amazon.comReferenced in the comparison table and product reviews above.
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