sentimany
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.
Quickstart
The goal is to whip up some sentiment models real quick. A demo is shown below.
import pandas as pd
from sentimany.sentiment import (
vader_sentiment,
textblob_sentiment,
onnx_sentiment,
roberta_sentiment,
nlptown_sentiment,
)
# 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
(df
.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.
85 Dec 09, 2022
2 Oct 21, 2022
4 Feb 15, 2022
1 Nov 02, 2021
7 Feb 26, 2022
2 Jan 09, 2022
8 Dec 18, 2022
2 Jun 25, 2022
2 Jan 12, 2022
7.5k Dec 30, 2022
2 Oct 27, 2021
48 Jan 28, 2021
1 Jan 01, 2022
6.1k Jan 04, 2023
31 Dec 25, 2022
2 Feb 04, 2022
1 Feb 15, 2022
1 Nov 24, 2021
8 Feb 27, 2022
5 Jan 03, 2022