Just a simple sentiment tool.

It just grabs a set of pre-made sentiment models that you can quickly use to attach sentiment scores to text. None of these sentiment models will be perfect, as none of them actually understand language, but they may serve well in human-in-the-loop kinds of labelling situations. Currently the tool only supports English models.


The goal is to whip up some sentiment models real quick. A demo is shown below.

import pandas as pd
from sentimany.sentiment import (

# Add some text to a pandas dataframe
texts = [
    "i like dogs",
    "i hate cats",
    "stroopwafels are amazing",
    "mcdondals is horrible",
df = pd.DataFrame({"text": texts})

# Apply each sentiment model and attach it as a new column
  .assign(vader = lambda d: vader_sentiment(d['text']), 
          textblob = lambda d: textblob_sentiment(d['text']),
          imdb_onnx = lambda d: onnx_sentiment(d['text'], "onnx/imdb-reviews.onnx"),
          amazon_onnx = lambda d: onnx_sentiment(d['text'], "onnx/amazon-reviews.onnx"),
          roberta = lambda d: roberta_sentiment(d['text']), 
          nlptown = lambda d: nlptown_sentiment(d['text'])))

This would result in a table that looks something like;

text vader textblob imdb_onnx amazon_onnx roberta nlptown
i like dogs 0.6806 0.5 0.5667 0.5770 0.9979 0.7335
i hate cats 0.2140 0.1 0.6835 0.3837 0.0016 0.3544
stroopwafels are amazing 0.7930 0.8 0.7374 0.8058 0.9985 0.9323
mcdondals is horrible 0.2288 0.0 0.1983 0.1522 0.0006 0.0605

Good to know

This is a repo made for utility for myself. Feel free to re-use, but don’t expect maintenance in the long term.

More-over though, keep in mind that sentiment models are imperfect and brittle. In particular check this short blogpost and this huggingface stream for more details.


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