R: Sentiment Analysis for Text, Image and Video using Transformer Models

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

## Documentation for package ‘transforEmotion’ version 0.1.6

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

## Help Pages

transforEmotion-package | transforEmotion-package
calculate_moving_average | Calculate the moving average for a time series
check_nvidia_gpu | Install Necessary Python Modules
conda_check | Check if the "transforEmotion" conda environment exists
delete_transformer | Delete a Transformer Model
dlo_dynamics | Dynamics function of the DLO model
emotions | Emotions Data
emoxicon_scores | Emoxicon Scores
emphasize | Generate and emphasize sudden jumps in emotion scores
generate_observables | Generate observable emotion scores data from latent variables
generate_q | Generate a matrix of Dynamic Error values for the DLO simulation
image_scores | Calculate image scores using a Hugging Face CLIP model
MASS_mvrnorm | Multivariate Normal (Gaussian) Distribution
neo_ipip_extraversion | NEO-PI-R IPIP Extraversion Item Descriptions
nlp_scores | Natural Language Processing Scores
plot_sim_emotions | Plot the latent or the observable emotion scores.
punctuate | Punctuation Removal for Text
rag | Retrieval-augmented Generation (RAG)
sentence_similarity | Sentiment Analysis Scores
setup_gpu_modules | Install GPU Python Modules
setup_miniconda | Install Miniconda and activate the transforEmotion environment
simulate_video | Simulate latent and observed emotion scores for a single "video"
stop_words | Stop Words from the _tm_ Package
tinytrolls | Russian Trolls Data - Small Version
transforEmotion | transforEmotion-package
transformer_scores | Sentiment Analysis Scores
video_scores | Run FER on a YouTube video using a Hugging Face CLIP model