73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
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from nltk.util import pr
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import pandas as pd
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import numpy as np
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier
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import re
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import nltk
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stemmer = nltk.SnowballStemmer("english")
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from nltk.corpus import stopwords
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import string
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stopword=set(stopwords.words('english'))
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data = pd.read_csv("datasets//twitter.csv")
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#Step 2 I will add a new column to this dataset as labels which will contain the values as: Hate Speech O. ffensive Language No Hate and Offensive
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data["labels"] = data["class"].map({0: "Hate Speech",
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1: "Offensive Language",
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2: "No Hate and Offensive"})
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data = data[["tweet", "labels"]]
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#Step 3 Now I will only select the tweet and labels columns for the rest of the task of training a hate speech detection model:
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def clean(text):
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text = str(text).lower()
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text = re.sub('\[.*?\]', '', text)
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text = re.sub('https?://\S+|www\.\S+', '', text)
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text = re.sub('<.*?>+', '', text)
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text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
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text = re.sub('\n', '', text)
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text = re.sub('\w*\d\w*', '', text)
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text = [word for word in text.split(' ') if word not in stopword]
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text=" ".join(text)
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text = [stemmer.stem(word) for word in text.split(' ')]
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text=" ".join(text)
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return text
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data["tweet"] = data["tweet"].apply(clean)
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#Step 5 Now I will create a function to clean the texts in the tweet column
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x = np.array(data["tweet"])
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y = np.array(data["labels"])
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cv = CountVectorizer()
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X = cv.fit_transform(x) # Fit the Data
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
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clf = DecisionTreeClassifier()
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clf.fit(X_train,y_train)
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#Step 6 Now let’s split the dataset into training and test sets and train a machine learning model for the task of hate speech detection
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x = np.array(data["tweet"])
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y = np.array(data["labels"])
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cv = CountVectorizer()
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X = cv.fit_transform(x) # Fit the Data
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
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clf = DecisionTreeClassifier()
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clf.fit(X_train,y_train)
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#print(data.head())
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#Step 7 Now let’s test this machine learning model to see if it detects hate speech or not
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sample = "Let's unite and kill all the people who are protesting against the government"
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data = cv.transform([sample]).toarray()
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print(clf.predict(data))
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