# Sentiment Analysis for Text, Image and Video using Transformer Models

## Documentation for package ‘transforEmotion’ version 0.1.7

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

transforEmotion-package | transforEmotion-package
.init_builtin_models | Initialize Built-in Vision Models
.vision_model_registry | Vision Model Registry for transforEmotion Package
add_vision_model | User-Friendly Vision Model Management Functions
as_rag_table | Convert RAG JSON to a table
calculate_moving_average | Calculate the moving average for a time series
check_findingemo_quality | Check FindingEmo Dataset Quality
check_nvidia_gpu | Install Necessary Python Modules
delete_transformer | Delete a Transformer Model
dlo_dynamics | Dynamics function of the DLO model
download_findingemo_data | Download FindingEmo-Light Dataset
emotions | Emotions Data
emoxicon_scores | Emoxicon Scores
emphasize | Generate and emphasize sudden jumps in emotion scores
evaluate_emotions | Evaluate Emotion Classification Performance
generate_observables | Generate observable emotion scores data from latent variables
generate_q | Generate a matrix of Dynamic Error values for the DLO simulation
get_vision_model_config | Get Vision Model Configuration
image_scores | Calculate image scores using a Hugging Face CLIP model
image_scores_dir | Calculate image scores for all images in a directory (fast batch)
is_vision_model_registered | Check if Vision Model is Registered
list_vision_models | List Available Vision Models
load_findingemo_annotations | Load FindingEmo-Light Annotations
map_discrete_to_vad | Map Discrete Emotions to VAD (Valence-Arousal-Dominance) Framework
map_to_emo8 | Map FindingEmo Emotions to Emo8 Labels
MASS_mvrnorm | Multivariate Normal (Gaussian) Distribution
neo_ipip_extraversion | NEO-PI-R IPIP Extraversion Item Descriptions
nlp_scores | Natural Language Processing Scores
parse_rag_json | Parse RAG JSON
plot.emotion_evaluation | Plot Evaluation Results
plot_sim_emotions | Plot the latent or the observable emotion scores.
prepare_findingemo_evaluation | Prepare FindingEmo Data for Evaluation
print.emotion_evaluation | Print method for emotion evaluation results
punctuate | Punctuation Removal for Text
rag | Retrieval-augmented Generation (RAG)
rag_json_utils | RAG JSON utilities
rag_sentemo | Structured Emotion/Sentiment via RAG (Small LLMs)
register_retriever | Register a custom retriever
register_vision_model | Register a Vision Model
remove_vision_model | Remove a Vision Model
sentence_similarity | Sentiment Analysis Scores
setup_gpu_modules | Install GPU Python Modules
setup_miniconda | Deprecated: Miniconda setup (use uv instead)
setup_modules | Setup Required Python Modules
setup_popular_models | Quick Setup for Popular Models
show_vision_models | Show Available Vision Models
simulate_video | Simulate latent and observed emotion scores for a single "video"
stop_words | Stop Words from the _tm_ Package
summary.emotion_evaluation | Summary method for emotion evaluation results
te_cleanup_default_venv | Remove reticulate's default virtualenv (r-reticulate)
tinytrolls | Russian Trolls Data - Small Version
transforEmotion | transforEmotion-package
transformer_scores | Sentiment Analysis Scores
vad_scores | Direct VAD (Valence-Arousal-Dominance) Prediction
validate_rag_json | Validate a RAG JSON structure
validate_rag_predictions | Validate RAG Emotion/Sentiment Predictions
video_scores | Run FER on a YouTube video using a Hugging Face CLIP model