Hate Speech Detection Using Machine Learning

				import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import re
import nltk
from nltk.util import pr
stemmer=nltk.SnowballStemmer("english")
from nltk.corpus import stopwords
import string
stopword=set(stopwords.words("english"))

# Load the data
data=pd.read_csv("twitter_data.csv")

# Preprocess the data
data['labels']=data['class'].map({0:"Hate speech",1:"Not offensive",2:"Neutral"})

# Select relevant columns
data = data[["tweet","labels"]]

# Tokenization and stemming
def tokenize_stem(text):
    tokens = nltk.word_tokenize(text)
    tokens = [word for word in tokens if word not in stopword]
    tokens = [word for word in tokens if word.isalpha()]
    tokens = [stemmer.stem(word) for word in tokens]
    return tokens

# Vectorize the text
vectorizer = CountVectorizer(tokenizer=tokenize_stem)
X = vectorizer.fit_transform(data['tweet'])

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, data['labels'], test_size=0.2, random_state=42)

# Train the classifier
classifier = DecisionTreeClassifier()
classifier.fit(X_train, y_train)

# Take input from the user
tweet = input("Enter your tweet: ")

# Vectorize the user input
tweet_vector = vectorizer.transform([tweet])

# Predict the label
prediction = classifier.predict(tweet_vector)

# Print the prediction
print(f"The tweet is classified as: {prediction[0]}")