https://github.com/prakhar-ff13/toxic-comments-classification
Predict the toxicity rating of comment made by the user.
https://github.com/prakhar-ff13/toxic-comments-classification
data-analysis data-science data-visualization deep-learning kaggle-competition kaggle-dataset lstm lstm-neural-networks machine-learning natural-language-processing nlp nlp-machine-learning python3
Last synced: 5 months ago
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Predict the toxicity rating of comment made by the user.
- Host: GitHub
- URL: https://github.com/prakhar-ff13/toxic-comments-classification
- Owner: Prakhar-FF13
- Created: 2019-05-28T11:50:31.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-05-28T11:53:06.000Z (over 6 years ago)
- Last Synced: 2025-05-07T10:15:10.325Z (5 months ago)
- Topics: data-analysis, data-science, data-visualization, deep-learning, kaggle-competition, kaggle-dataset, lstm, lstm-neural-networks, machine-learning, natural-language-processing, nlp, nlp-machine-learning, python3
- Language: HTML
- Size: 1.03 MB
- Stars: 43
- Watchers: 1
- Forks: 33
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
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README
# Toxicity Classification:
## 1. Business Problem:
**Source:** https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification
**Description:** https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview/description
**Problem Statement:** Given a comment made by the user, predict the toxicity of the comment.
## 2. Machine Learning Problem Formulation:
### 2.1 Data:
- Source: https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data
- We have one single csv file for training and one cvs file to test.
- Columns in train data:
- Comment_text: This is the data in string format which we have to use to find the toxicity.
- target: Target values which are to be predicted (has values between 0 and 1)
- Data also has additional toxicity subtype attributes: (Model does not have to predict these)
- severe_toxicity
- obscene
- threat
- insult
- identity_attack
- sexual_explicit
- Comment_text data also has identity attributes carved out from it, some of which are:
- male
- female
- homosexual_gay_or_lesbian
- christian
- jewish
- muslim
- black
- white
- asian
- latino
- psychiatric_or_mental_illness
- Apart from above features the train data also provides meta-data from jigsaw like:
- toxicity_annotator_count
- identity_anotator_count
- article_id
- funny
- sad
- wow
- likes
- disagree
- publication_id
- parent_id
- article_id
- created_date
### 2.2 Example Datapoints and Labels:
**Comment:** i'm a white woman in my late 60's and believe me, they are not too crazy about me either!!
- Toxicity Labels: All 0.0
- Identity Mention Labels: female: 1.0, white: 1.0 (all others 0.0)
**Comment:** Why would you assume that the nurses in this story were women?
- Toxicity Labels: All 0.0
- Identity Mention Labels: female: 0.8 (all others 0.0)
**Comment:** Continue to stand strong LGBT community. Yes, indeed, you'll overcome and you have.
- Toxicity Labels: All 0.0
- Identity Mention Labels: homosexual_gay_or_lesbian: 0.8, bisexual: 0.6, transgender: 0.3 (all others 0.0)
### 2.3 Type of Machine Learning Problem:
We have to predict the toxicity level(target attribute). The values range from 0 to 1 inclusive. This is a regression problem. It can also be treated as a classification problem if we take every value below 0.5 to be non-toxic and above it to be toxic, we would then get a binary classification problem.
### 2.4 Performance Metric:
The competition will use ROC_AUC as the metric after converting the numeric target variable into a categorical variable by using a threshold of 0.5. Any comment above 0.5 will be assumed to be toxic and below it non-toxic. For our training and evaluation we will use the MSE(Mean Squared Error).
More on evaluation: https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview/evaluation
### 2.5 Machine Learning Objectives and Constraints:
**Objectives:** Predict the toxicity of a comment made by the user. (0 -> not toxic, 1 -> highest toxicity level)
**Constraints:**
- The model should be fast to predict the toxicity rating.
- Interpretability is not needed.