plot_sim_emotions

Function to plot the latent or the observable emotion scores.

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>.

Aleksandar Tomašević

transforEmotion

Sentiment Analysis for Text, Image and Video using Transformer
Models

Alexander Christensen

Hudson Golino

plot_sim_emotions function

<dl><dt>df</dt>
<dd>Data frame.
The data frame containing the latent and observable variables created by the <code>simulate_video</code> function.</dd>
<dt>mode</dt>
<dd>Character.
The mode of the plot. Can be either 'latent', 'positive' or 'negative'.</dd>
<dt>title</dt>
<dd>Character.
The title of the plot. Default is an empty title, ' '.</dd></dl>

Arguments

Plot the latent or the observable emotion scores. — plot_sim_emotions

<dl>

<dt>df</dt>
<dd>Data frame.
The data frame containing the latent and observable variables created by the <code>simulate_video</code> function.</dd>


<dt>mode</dt>
<dd>Character.
The mode of the plot. Can be either 'latent', 'positive' or 'negative'.</dd>


<dt>title</dt>
<dd>Character.
The title of the plot. Default is an empty title, ' '.</dd>

</dl>

plot_sim_emotions: Plot the latent or the observable emotion scores.

Description

Usage

Value

Arguments