emopoint 0.1.3

A library for mapping text into a multidimensional embedding space representing emotion

Extract emotional information from embeddings.

When working with LLMs, various embedding models capture emotional information that might be useful to work with (or without!).

An emopoint is a simplified embedding with interpretable dimensions:

1. joy vs sadness
2. anger vs fear
3. disgust vs surprise

So, for example OpenAI's text-embedding-3-small returns embeddings with 1536 dimensions. This library will convert those into 3 dimensions, losing most information except for what directly relates to emotion.

This library enables two modes:

1. Isolate emotion, converting it into 3D emopoint vectors
2. Remove emotion, stay in original dimensionality

Install

Install using your language's package manager:

JavaScript/TypeScript via NPM

npm i emopoint

and then use it

const { MODELS } = require('emopoint');

console.log(MODELS.ADA_2);

Python via PyPi

pip install emopoint

and then use it

from emopoint import MODELS

embedding = get_embeddings("James was maaaaaad")
emopoint = MODELS.ADA_3_SMALL.emb_to_emo(embedding)

Go

go get github.com/tkellogg/emopoint/go/emopoint

and then use it

import (
	emo "github.com/tkellogg/emopoint/go/emopoint"
)

func main() {
	var embeding []float32 = getEmbeddings("James was maaaaaad")
	var emopoint []float32 = emo.ADA_3_SMALL.EmbeddingToEmopoint(embedding)
}

Functions

All 3 languages have these capabilities:

• Convert embedding to emopoint — Convert an embedding (e.g. 1536 dimensions for text-embedding-3-small) to 3-dimensional space, called emopoint space that represents only emotion and nothing else.
• Remove emotion — Take an embedding and keep it in the same dimensionality, but subtract emotional information

From these operations, there's a lot more you can do:

• Get the portion of emotional information in text — Calculate the magnitude of the embedding (should be always 1.0) and subtract the magnitude of the result of remove_emotion(embedding). The result is a scalar float that represents the portion of the meaning of the text that was dedicated to emotion, as the embedding model understood it.
• Cluster on emotion — Convert to emopoint space and run a K-Means clustering algorithm
• Semantic search on emotion only — Convert to emopoint space and store in a vector database. This matches text based only on the emotional content, ignoring all factual and subjective information.
• Semantic search without emotion — Same as before, but store the result of remove_emotion(embedding). This removes noise introduced by emotion, creating closer matches and potentially enhancing the search accuracy.
• Analytics & visualizations on emotional magnitude — Calculate the magnitudes of emopoints for several texts, e.g. sections of a speech or tweets, and create visualizations on just the magnitude (portion of information dedicated to emotion).
• Analytics & visualizations on emotions — Same as before, but instead of calculating the magnitude, visualize the points in 3D emopoint space. Observe how some texts lean toward anger or joy. Analyze how emotions ebb & flow throughout a speech, and contrast that to the informational content (maybe use K-Means clustering on original content to classify the content and display those classifications as colors in a 3D scatter plot).