Results for “sentiment-classification”

Train, evaluate, export, and run inference for pose classification models using ST-GCN on skeleton keypoint sequences.

Monitor brand mentions, analyze sentiment, and identify trends across social platforms. Use when the user needs to analyze social data, track brand health, or understand audience sentiment.

Extracts structured data from Reddit posts and comments via the BrowserAct API for competitor analysis, brand sentiment tracking, and market research.

Validates the Emotional Attitude Score (EAS) metric by measuring its consistency with human judgment on word-level sentiment polarity, using the AmbGIMT dataset and pairwise score comparisons.

Evaluates speech language models on beyond-semantic speech attributes such as dialect comprehension, multi-turn context memory, emotion perception, age-aware response generation, and non-verbal cue handling, reporting accuracy and judge-based scores.

Evaluates image captions by converting them into scene graphs and computing an F-score over semantic propositions, measuring how well a generated caption captures the meaning of an image compared to human references.

Train, evaluate, distill, quantize, export, and run inference for PyTorch-based TAO image classification models with support for multiple backbones.

Classifies HTML pages, forms, and fields using machine learning to detect page types, form types, and field types from HTML content or URLs.

Rank a topic's user-flow branches by proof priority (value × risk × frequency) right after user-flow-map, ordering the branches, gating variation breadth, and promoting or pruning flows so state-model and ux-variations grow the tree in proof order — writes only existing flow-tree ordering fields, no schema change.

`analysis-agent`/`task-agent`/`review-agent`: use when symptoms, logs, metrics, regressions, or incidents need cause analysis; skip when no diagnosis decision exists.

Guía para elegir y aplicar tests de hipótesis con SciPy, verificando supuestos, interpretando p-values y tamaño del efecto, y evitando falsos positivos.