Sentiment Analysis Datasets for NLP and CX AI
Domain-specific sentiment corpora with aspect-level labels, intensity scores, and multilingual coverage. Built for fine-tuning sentiment models on your product feedback, customer support, and brand monitoring use cases.
20+
Years in AI training data
1,000+
Language locales
1M+
Hours of video annotated
ISO
27001 certified
The Challenge
Beyond General Sentiment Benchmarks
SST-2 and IMDB established binary sentiment classification baselines. Production sentiment AI for customer experience, brand monitoring, and product feedback requires aspect-level granularity and domain-specific vocabulary those benchmarks lack.
Customer experience AI must distinguish sentiment toward specific product attributes, service dimensions, and brand touchpoints. Healthcare feedback AI must handle medical terminology and patient communication styles. General sentiment models trained on movie reviews fail on these specialized domains.
Off-the-shelf sentiment datasets suffer from domain language gaps (movie and restaurant review language differs from B2B SaaS feedback, clinical patient satisfaction, or financial analysis commentary) and aspect-level annotation gaps.
LXT builds custom sentiment analysis datasets with aspect-level granularity, domain vocabulary, and the text genre distribution matching your sentiment AI application. We deliver labeled corpora that train models to understand sentiment in your specific domain.
Why Teams Upgrade
Limitations of Public Sentiment Analysis Datasets
Standard benchmarks serve research well. Production deployments need more.
| Dataset | Primary Limitation | Impact |
|---|---|---|
| SST-2 | Film review sentences only; binary positive/negative only; no aspect-level annotation; formal written English with consumer media bias | Film reviews |
| IMDB | Long movie reviews; binary labels; no fine-grained aspect annotation; limited domain applicability outside consumer media | Binary only |
| Amazon Reviews | Product review bias; English-dominant; aspect annotation inconsistent across product categories; star rating as label proxy | Star-label proxy |
| SemEval ABSA | Restaurant and laptop domains only; English focused; limited multilingual or domain-specific aspect taxonomies | Two domains |
| Yelp Polarity | Restaurant and local service only; binary polarity; no aspect-level annotation; limited non-consumer domains | Restaurant-only |
Not sure which specs you need?
Our data specialists help you scope the right dataset for your model architecture.
Configurable Specifications
Specs Built Around Your Model
Public datasets come fixed. Yours is configured for your architecture, environment, and use case.
Text Domains
Corpus Coverage
- Source Types: Reviews, support tickets, social media, surveys, and call transcripts
- Domain Language: Product-specific terminology, domain jargon, and brand vocabulary
- Languages: Multilingual sentiment across 30+ languages and regional variants
Annotation Granularity
Label Depth
- Sentence-Level: Overall positive, negative, neutral, and mixed sentiment labels
- Aspect-Level: Sentiment toward specific product attributes, features, or service dimensions
- Intensity Scores: 5-point or 7-point sentiment intensity scales beyond binary classification
Label Quality
Annotation Standards
- Expert Annotators: Native speakers with domain knowledge for specialized content
- IAA Measurement: Inter-annotator agreement measured per aspect category
- Edge Case Guidelines: Documented handling of sarcasm, negation, and conditional sentiment
Need a custom configuration?
We've built datasets across dozens of domains and use cases. Let's scope yours.
Capturing Complexity
Edge Cases in Sentiment Annotation
High-accuracy models handle rare attributes that public datasets miss.
Sarcasm and Ironic Expression
Sarcasm produces lexically positive text with negative sentiment. Expert annotators identify sarcastic expressions and flag them for sentiment polarity reversal in annotation.
Mixed and Compound Sentiment
Customer feedback often contains both positive and negative sentiment toward different aspects simultaneously. Aspect-level annotation captures this complexity that document-level labels obscure.
Domain-Specific Negation Patterns
Technical domains use domain-specific negation patterns. Annotation guidelines document negation handling specific to your domain's language conventions.
Low-Intensity and Neutral Borderline Cases
Weak sentiment expression near the neutral boundary produces annotator disagreement. Intensity calibration sessions and neutral handling guidelines ensure consistent borderline annotation.
Ground Truth Quality
Human-in-the-Loop Annotation
Precise annotation bridges raw data and learnable signal. Expert annotators deliver precision automated tools can't match.
Sentiment Label Annotation
Expert annotators assign sentiment polarity and intensity labels at sentence and aspect level. Native speaker annotation for non-English languages.
Aspect Category Labeling
Domain-specific aspect taxonomy annotation identifying which product or service dimension each sentiment expression targets.
Intensity and Agreement Scores
Per-annotation intensity scores and multi-annotator agreement measures for soft-label training and annotation uncertainty modeling.
Industry Applications
Sentiment Datasets for Your Domain
Custom taxonomies and collection protocols for specific deployment contexts.
Customer Experience
NPS prediction, CSAT analysis, churn signals
E-Commerce
Product review analysis, return prediction
Financial AI
Earnings call sentiment, analyst report tone
Brand Monitoring
Social media sentiment, reputation management
Healthcare CX
Patient satisfaction, clinical feedback
SaaS Products
Feature feedback, churn signal detection
Legal AI
Case sentiment, tone analysis
Multilingual CX
Global brand sentiment across languages
Compliance & Ethics
Secure and Ethical Data Collection
Data collection involving people and sensitive content requires robust security, compliance, and ethical protocols at every stage.
Global Demographic Reach
Collection across 1,000+ locales and diverse demographics to prevent algorithmic bias in your deployed models.
ISO 27001 Certified
Sensitive projects processed in certified secure facilities meeting the highest information security standards.
GDPR & Privacy Compliance
All collection and annotation protocols vetted for consent and privacy. Legally robust for global deployment.
Frequently Asked Questions
Sentiment Analysis Dataset FAQs
Can you build aspect-level sentiment datasets for our domain?+
Yes. We develop custom aspect taxonomies from your product features or service dimensions and annotate sentiment at aspect level with your vocabulary guidelines.
Do you support multilingual sentiment annotation?+
Yes. We annotate sentiment in 30+ languages using native speaker annotators. Regional language variants and dialect coverage are available for global CX applications.
Can you annotate our existing customer feedback data?+
Yes. We annotate proprietary feedback corpora under a data processing agreement. Existing text data is often the most efficient source for domain-accurate training data.
How do you handle sarcasm and irony?+
Annotators are trained to identify sarcastic expressions with worked examples. Sarcasm flags are included as metadata so you can handle these cases differently in training.
What output formats do you support?+
CSV and JSON with text, label, aspect, and intensity columns. Compatible with Hugging Face datasets format, spaCy DocBin, and custom formats for your training pipeline.
What does a custom sentiment dataset cost?+
Projects range from $8K for focused single-domain corpora (5,000-20,000 sentences) to $60K+ for large aspect-level, multilingual sentiment collections.
Can you provide intensity and confidence scores alongside polarity labels?+
Yes. Multi-annotator intensity scores and agreement-based confidence values are available, enabling soft-label training that captures annotation uncertainty.
Get Started
Scope Your Custom Sentiment Analysis Dataset
Share your text domain, aspect taxonomy, and language requirements. An NLP data specialist will provide a detailed proposal within 48 hours.
Please provide us with the details of your inquiry and one of our team members will be in touch.
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