Sentiment Analysis Using Google's Text Embeddings.

sentiments.py

import vertexai.language_models as v
import jax.numpy as jnp
import jax.numpy.linalg as jl

model = v.TextEmbeddingModel.from_pretrained(
    "textembedding-gecko"
)

sentiment_checks = [
    "This is very good. I loved it! Thanks",
    "This was bad. I hated it! Yuck"
]

sce = model.get_embeddings(sentiment_checks)

prompts = [
    "Wow! Cool",
    "Dumbest thing ever! I have nightmares now.",
    "You are dumb!"
]

positive = jnp.array(sce[0].values)
negative = jnp.array(sce[1].values)

prompt_embeddings = model.get_embeddings(prompts)

def similarity(x, y):
    return x @ y.T / (jl.norm(x) * jl.norm(y))

for i, prompt in enumerate(prompt_embeddings):
    prompt = jnp.array(prompt.values)
    v1 = similarity(prompt, positive)
    v2 = similarity(prompt, negative)
    print(prompts[i])
    print("Positive" if v1 > v2 else "Negative")
    print("---")