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A basic and simple yet powerful Python library to detect toxicity/profanity of a review or list of reveiws.

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cuss_inspect

pypi

A simple yet powerful library to predict toxicity/profanity of a review/comment or list of reviews/comments.

How It Works

cuss_inspect is a logistic regression based model trained on 180K+ reviews and tested on 24K+ reviews. The library does not uses any specific wordlist/swear-words-list but is able to detected most of the swear words easily.

Performance

1 Prediction (ms) 10 Predictions (ms) 100 Predictions (ms) 1000 Predictions (ms) 10000 Predictions (ms)
cuss_inspect 0.2 0.3 0.8 4.3 24.7

Accuracy

The accuracy,precision and recall are quite impressive as compared to other models. Logistic regression for text classification outperforms many other classifcation algorithms such as SVC,Decision Tree and Naive Bayes.

Precision Recall F1 Score
0 0.84 0.94 0.89
1 0.99 0.96 0.98
Accuracy 0.96
macro avg 0.91 0.95 0.93
weighted avg 0.96 0.96 0.96

Receiver Operating Characteristics

ROC Curve

Installation

$ pip install cuss_inspect

Usage

from cuss_inspect import predict, predict_prob

# for simple string
text_0 = "this is simple review. you have done a good job"
print(predict(text_0))
# [0]
print(predict_prob(text_0)
# [0.05]

text_1 = "son of a bitch"
print(predict(text_1))
# [1]
print(predict_prob(text_1)
# [1.]

# for list of inputs
test = ['who are you?' , 'what do you want?' , 'son of a dog' , 'how the hell can you say that' , 'fuck it']
print(predict(test))
# [0 0 1 1 1]
print(predict_prob(test))
# [0.12 0.22 0.55 0.96 1.]

*predict() and predict_prob return numpy arrays.

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  • Python 100.0%