LLM Evaluation Checklist for Sentiment Analysis Teams in 2026

Measure classification accuracy for positive, negative, and neutral sentiment on a balanced test set of at least 500 samples per class. Neutral sentiment is consistently the hardest to classify. Report per-class precision, recall, and F1 alongside overall accuracy.

If using a 5-point or continuous sentiment scale, measure mean absolute error between predicted and human-labeled sentiment intensity. Fine-grained scoring is more useful than binary classification but harder to get right. Validate against multi-annotator agreement.

Build and maintain domain-specific evaluation sets for each vertical you serve: product reviews, social media, support tickets, financial news, and medical notes. Sentiment expressions vary dramatically across domains. Generic benchmarks hide domain-specific weaknesses.

Create a dedicated test set of at least 200 sarcastic and ironic statements annotated with their actual (non-literal) sentiment. Sarcasm inverts polarity and is the most common source of misclassification in sentiment systems. Track sarcasm detection accuracy separately.

Test classification of texts containing both positive and negative sentiments (e.g., 'Great product but terrible customer service'). The model should either identify the mixed nature or correctly prioritize the dominant sentiment. Evaluate with 100+ mixed-sentiment samples.

Test detection of sentiment expressed without explicit sentiment words: 'I returned the product the next day' implies negative sentiment. Implicit sentiment is common in professional communication and reviews. Build a targeted test set of 100+ implicit examples.

Verify correct sentiment classification when negation is involved: 'not bad', 'never disappointing', 'could not be worse'. Negation is a classic NLP challenge that LLMs still frequently mishandle. Test with at least 50 negation patterns.

Test handling of comparative statements: 'Product A is better than Product B' should assign positive sentiment to A and relative negative to B. Comparative expressions require understanding of the relationship. Evaluate with 50+ comparative samples.

Evaluate sentiment classification when text includes emojis, emoticons, and kaomoji that modify or replace verbal sentiment. Emojis can reinforce, contradict, or solely carry the sentiment of a message. Test with emoji-heavy social media content.

Test whether the model detects sentiment shifts within a single text: reviews that start positive and end negative, or complaints that become appreciative. Whole-text classification misses these shifts. Evaluate sentence-level vs. document-level accuracy.

Measure precision and recall for extracting aspects (features, topics, attributes) mentioned in text. Missed aspects mean lost insights; hallucinated aspects add noise. Evaluate against a gold-standard annotated set of 200+ texts with expert-identified aspects.

Evaluate the accuracy of associating the correct sentiment with each extracted aspect. 'Great food but slow service' should yield positive for food and negative for service. This is the core metric for aspect-level systems. Test with 200+ multi-aspect examples.

When multiple entities are mentioned in a text, verify that sentiment is correctly attributed to each entity. 'Company A outperforms Company B' requires entity-level sentiment assignment. Build entity-specific test cases.

Test whether extracted aspects are correctly mapped to standardized category taxonomies. 'Speed', 'performance', 'responsiveness', and 'lag' should all map to the same category. Measure taxonomy mapping accuracy across synonym variations.

Evaluate detection of aspects that are implied but not explicitly stated: 'I waited 45 minutes' implies the 'wait time' aspect without naming it. Implicit aspects are common in natural language and critical for complete analysis. Test with 50+ implicit aspect examples.

Test whether the system correctly filters out irrelevant aspects that appear in text but are not germane to the analysis context. A product review mentioning weather should not extract 'weather' as a product aspect. Measure filtering precision.

Evaluate the accuracy of aspect frequency counts and trend detection over time. Incorrect frequency counts mislead product teams about what customers care about. Compare automated counts against manual annotation on a sample.

Test the quality of aggregated aspect-level sentiment across many reviews. Individual extraction errors should average out, but systematic biases compound. Verify aggregate aspect sentiment against human-computed averages.

If aspects have hierarchical relationships (food > taste > spiciness), evaluate whether the model correctly assigns sentiment at each hierarchy level. Hierarchical analysis provides both summary and detail views. Test across 3+ hierarchy levels.

When analyzing competitive mentions, verify that aspect-level sentiment is correctly split between your product and competitors mentioned in the same text. Misattribution corrupts competitive intelligence. Test with 50+ competitive mention samples.

Evaluate sentiment accuracy independently for each supported language using language-specific test sets with native-speaker annotations. Never assume that accuracy transfers across languages. Track per-language F1 scores and compare against the primary language baseline.

Test accuracy on text that switches between languages within a single message, which is common in multilingual communities and social media. Code-switched text often confuses language-specific models. Build a test set of 100+ code-switched samples.

Evaluate whether the model accounts for cultural differences in sentiment expression. Japanese negative feedback tends to be more indirect than American feedback; German directness is not negative sentiment. Calibrate per-culture sentiment thresholds.

Test sentiment accuracy across different scripts: Latin, Cyrillic, Arabic, Chinese, Japanese, Korean, and Devanagari. Script-specific challenges include right-to-left text, character tokenization, and mixed-script content. Benchmark per script family.

Evaluate accuracy on informal text with slang, abbreviations, and internet language for each supported language. Social media and messaging contain heavy informal language. Build slang-rich test sets per language, updated annually as slang evolves.

If using translation to a common language before analysis, measure the sentiment accuracy loss from translation. Translation can neutralize, invert, or lose sentiment nuances. Compare translate-then-analyze versus native language analysis accuracy.

Test accuracy across dialects of the same language: Brazilian vs. Portuguese Portuguese, simplified vs. traditional Chinese, Latin American vs. Castilian Spanish. Dialect-specific vocabulary and expressions affect sentiment classification.

Specifically evaluate performance on low-resource languages where training data is limited. LLMs may provide superficially reasonable but actually poor sentiment analysis in languages they were minimally trained on. Set honest quality expectations per language.

Verify that the same sentiment expressed in different languages receives the same classification. Translate a test set into all supported languages and compare consistency. Inconsistency reveals language-specific biases.

If providing sentiment analysis results to users in multiple locales, verify that output labels, explanations, and visualizations are correctly localized. Poor localization undermines trust in the analysis. Test output quality per locale.

Measure sentiment analysis latency per message at P50, P95, and P99. Real-time applications (live chat sentiment, social media monitoring) require sub-100ms processing. Batch applications can tolerate higher latency. Set latency SLAs per use case.

Load test the sentiment analysis system at peak expected volume: product launches, viral events, crises, and seasonal peaks. Measure at 3x, 5x, and 10x normal volume. Identify the throughput ceiling and plan scaling accordingly.

Measure throughput and cost for batch sentiment analysis on large datasets (millions of messages). Batch processing should leverage batching optimizations for significant cost savings over individual processing. Compare batch vs. real-time cost per message.

Test the reliability of real-time streaming sentiment analysis: message ordering, exactly-once processing, and graceful handling of bursts. Missed or duplicated messages corrupt sentiment trends. Monitor pipeline health metrics continuously.

Calculate the fully loaded cost per sentiment analysis including LLM tokens, embedding generation, and infrastructure. At high volume, small per-message costs compound. Evaluate whether lighter models can maintain acceptable accuracy at lower cost.

Implement and evaluate caching for frequently analyzed content: common phrases, standard replies, and template messages. Customer support and social media contain significant content repetition. Measure cache hit rates and quality impact.

Measure how quickly infrastructure auto-scales in response to volume spikes and how latency is affected during scaling events. Slow auto-scaling causes latency spikes during sudden volume increases. Target scaling within 60 seconds.

Evaluate whether routing simple sentiment tasks to lighter models and complex tasks (sarcasm, mixed sentiment, multilingual) to larger models improves cost-efficiency without significant quality loss. Implement and measure multi-model routing.

Implement monitoring for the complete data pipeline from message ingestion to sentiment output: data completeness, processing latency, and error rates at each stage. Silent pipeline failures cause incomplete analysis. Alert on anomalies.

Test the ability to reprocess historical data with updated models or configurations. When models improve, reprocessing historical data corrects past inaccuracies. Measure reprocessing throughput and compare old vs. new results.

Calculate inter-annotator agreement (Krippendorff's alpha or Cohen's kappa) for your evaluation datasets. Sentiment is inherently subjective, and disagreement among human annotators sets the upper bound for model accuracy. Target alpha > 0.7 for usable annotations.

Audit your evaluation datasets for representation across domains, languages, demographics, text lengths, and sentiment distributions. Skewed evaluation sets produce misleading accuracy numbers. Rebalance datasets to reflect production traffic distribution.

Build an automated regression testing pipeline that runs on every model update, prompt change, or configuration modification. Include tests for known failure modes and edge cases. Block deployments that fail regression tests.

Implement systematic sampling of production sentiment predictions for human quality review. Random samples catch quality issues that test sets miss. Review at least 100 production predictions per week with human annotators.

Monitor for distribution shifts in input data that could degrade model performance: new vocabulary, changed topics, shifted sentiment distribution, or new languages appearing in the data. Alert when drift metrics exceed thresholds.

Categorize every sentiment error into a structured taxonomy: negation errors, sarcasm misses, domain confusion, entity misattribution, and intensity miscalibration. Tracking error category trends reveals where to invest improvement effort.

Maintain comparison benchmarks against simple baselines (lexicon-based, rule-based) and competitive LLM alternatives. If your system does not significantly outperform a simple dictionary lookup, the LLM overhead is not justified. Update comparisons quarterly.

Implement mechanisms for end users to correct sentiment classifications and feed corrections back into evaluation and training. User corrections are the highest-quality labels available. Track correction volume and integrate systematically.

Test model resilience to adversarial inputs designed to manipulate sentiment scores: character substitutions, strategic typos, and deliberately misleading text. Adversarial robustness matters especially for competitive monitoring use cases. Run quarterly adversarial audits.

Track how your primary evaluation metrics have evolved over time as you have made improvements. Diminishing returns on current metrics may signal the need to shift focus to underserved dimensions like aspect-level quality or multilingual accuracy.