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Cancellation Policy: If you cancel your registration two weeks or more before the course is scheduled to begin, you are entitled to receive your choice of either a credit for a future seminar (which can be applied toward any of our courses) or a refund of the registration fee (minus a processing fee of $50). 

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Hudson Golino, Ph.D. and Aleksandar Tomašević, Ph.D.

This hands-on seminar presents a practical, end-to-end workflow to study emotions and sentiment in text, images, and video using transformer AI models entirely within R via the transforEmotion package. You will learn how to run zero-shot emotion classification for text (BERT-style encoders), detect facial-expression cues in images and video (CLIP), and generate succinct, structured rationales with small LLMs to support interpretation and reporting. No deep learning background or GPU access is required.

By the end, you will be able to select appropriate models for your research question, score multimodal data with transforEmotion, export analysis-ready tables, and evaluate results with transparent, publication-ready methods. You’ll walk away with the tools to:

• • Analyze text with BERT-style encoders using zero-shot labels you define.
  • Detect emotion cues in facial images and short video clips using CLIP-based pipelines.
  • Generate concise, structured explanations with small LLMs to improve interpretability.

We’ll emphasize a streamlined, reproducible approach for social-science workflows in R. You’ll install and configure models through transforEmotion, then reproduce the full pipeline: load sample assets, score text with BERT encoders, classify images and video frames with CLIP, and optionally add short, structured rationales from small LLMs. You will design theory-aligned label sets (e.g., basic emotions, valence–arousal) and document settings to ensure transparent reporting.

The course balances method and practice. We’ll compare encoder vs. decoder architectures and explain how multimodal embeddings represent signals across text and vision. You will craft zero-shot label prompts, interpret similarity scores, and apply lightweight validation checks (accuracy, F1, confusion matrices). We’ll also cover practical issues: short texts, multilinguality, class imbalance, bias and privacy, face selection, and frame sampling strategies for video.

A capstone ties everything together on a curated subset of the MAFW dataset. You will combine text scores (BERT), image/video outputs (CLIP), and optional LLM rationales, then evaluate cross-modal alignment, run metrics, and iterate on labels and sampling choices to resolve disagreements.