An NLP model that determines if a Tweet is "Toxic or Delicious"
Imports:
import logging import pandas as pd import numpy as np from numpy import random import gensim import nltk from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.metrics import accuracy_score, confusion_matrix import matplotlib.pyplot as plt from nltk.corpus import stopwords import re from bs4 import BeautifulSoup
Language: Python 3.8
Purpose:
Use the SVM (Support Vector Machines) algorithm to classify Tweet text. Use Stochastic Gradient Descent neural network to optimize text classification.
“Gradient descent is an iterative algorithm, that starts from a random point on a function and travels down its slope in steps until it reaches the lowest point of that function.” This means that we just use randomness in our gradient descent. This article explains it beautifully: https://towardsdatascience.com/stochastic-gradient-descent-clearly-explained-53d239905d31
Dataset:
Data is from: https://www.kaggle.com/shashank1558/preprocessed-twitter-tweets?select=processedNegative.csv I added in some of my own flavor of course.