Hate Speech Detection Using Machine Learning Project

                       INTRODUCTION

Natural language processing (NLP) is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language.

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Complete Code 😃👇

from nltk.util import pr

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

stemmer = nltk.SnowballStemmer("english")

from nltk.corpus import stopwords

import string

stopword=set(stopwords.words('english'))

data = pd.read_csv("twitter.csv")

print(data.head())

data["labels"] = data["class"].map({0: "Hate Speech", 

                                    1: "Offensive Language", 

                                    2: "No Hate and Offensive"})

print(data.head())

data = data[["tweet", "labels"]]

print(data.head())

def clean(text):

    text = str(text).lower()

    text = re.sub('\[.*?\]', '', text)

    text = re.sub('https?://\S+|www\.\S+', '', text)

    text = re.sub('<.*?>+', '', text)

    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)

    text = re.sub('\n', '', text)

    text = re.sub('\w*\d\w*', '', text)

    text = [word for word in text.split(' ') if word not in stopword]

    text=" ".join(text)

    text = [stemmer.stem(word) for word in text.split(' ')]

    text=" ".join(text)

    return text

data["tweet"] = data["tweet"].apply(clean)

x = np.array(data["tweet"])

y = np.array(data["labels"])

cv = CountVectorizer()

X = cv.fit_transform(x) # Fit the Data

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

clf = Decision.........

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