Sentiment Analysis for Text, Image and Video using Transformer Models
Calculate the moving average for a time series
Install Necessary Python Modules
Check if the "transforEmotion" conda environment exists
Delete a Transformer Model
Dynamics function of the DLO model
Emoxicon Scores
Generate and emphasize sudden jumps in emotion scores
Generate observable emotion scores data from latent variables
Generate a matrix of Dynamic Error values for the DLO simulation
Calculate image scores using a Hugging Face CLIP model
Multivariate Normal (Gaussian) Distribution
Natural Language Processing Scores
Plot the latent or the observable emotion scores.
Punctuation Removal for Text
Retrieval-augmented Generation (RAG)
Sentiment Analysis Scores
Install GPU Python Modules
Install Miniconda and activate the transforEmotion environment
Simulate latent and observed emotion scores for a single "video"
transforEmotion--package
Sentiment Analysis Scores
Run FER on a YouTube video using a Hugging Face CLIP model
Implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> and default image/video pipeline is Open AI's CLIP <https://huggingface.co/openai/clip-vit-base-patch32>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>.