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NLP Project for Beginners on Text Processing and Classification

Have you ever questioned how objects interpret or categorize text? This project provides insight into text processing as well as text classification with the use of NLP. It is a beginner-focused project where one gets the theory but also has practice in creating a machine-learning model. Hence you will work with NLTK, Scikit-learn, Pandas and so on and learn how to clean, tokenize, and organize information into different categories.

Project Overview

In this project, you will delve into the machine’s ability to read and understand text with ease and classify it most appropriately. You will learn what natural language processing (NLP) is and the processes involved in preparing the raw text for further analysis. Libraries such as NLTK and Scikit-learn will be used to quantify the text into figures that will be used by the machine learning models.

Using CountVectorizer and TfidfVectorizer, you will learn how to perform feature extraction and use Logistic Regression to create a classifier. What’s the objective? Classify the emotion of a particular text as positive, negative, or neutral. In between, you will also check the performance of your model with the help of classification accuracy and confusion matrices so that it is not performing poorly.

This project will leave you with a functional text classifier and basic skills in using NLP techniques. This includes the understanding of how sentiment analyzers or rating systems work using reviewing web content. Hence this project is all about comprehending the text classification tasks!

Prerequisites

This project is beginner-friendly, but having some basic knowledge will make things smoother and more fun! Here’s what you need:

  • Familiarity with Python programming and libraries like Pandas, Numpy, and Matplotlib.
  • Having prior knowledge of concepts of machine learning, such as Logistic Regression.
  • Familiarity with libraries such as NLTK library and the SKlearn library.
  • Basic Familiarity with NLP concepts and techniques to apply to the text data.

Approach

This project applies a systematic approach to text classification using Natural Language Processing (NLP). It begins with installing essential libraries like Scikit-learn, Numpy, Pandas, NLTK, and Seaborn. Text data is then preprocessed through tokenization, stopword removal, and stemming using NLTK. Features are extracted using CountVectorizer and TfidfVectorizer to convert text into numerical representations suitable for machine learning models. Logistic Regression is utilized for text classification, with the model’s performance evaluated using metrics like accuracy, confusion matrix, and classification reports. Results are visualized using Matplotlib and Seaborn for clear interpretation. This hands-on approach ensures a practical understanding of NLP fundamentals. ​​

Workflow and Methodology

Workflow

  • Install necessary libraries like Scikit-learn, NLTK, Pandas, and Matplotlib for text processing and analysis.
  • Preprocess raw text data by tokenizing, removing stopwords, and applying stemming techniques using NLTK.
  • Convert text into numerical features using CountVectorizer and TfidfVectorizer for model training.
  • Split the dataset into training and testing sets using Scikit-learn's train_test_split function.
  • Train a Logistic Regression model to classify text sentiment as positive, negative, or neutral.
  • Evaluate the model’s performance with metrics like accuracy, confusion matrix, and classification reports.
  • Visualize results and metrics using Matplotlib and Seaborn for better insights.

Methodology

  • Data preprocessing ensures text is clean and standardized for effective analysis.
  • Feature extraction transforms text data into numerical formats suitable for machine learning models.
  • Logistic Regression is used for its simplicity and effectiveness in classification tasks.
  • Model evaluation measures accuracy and provides insights into prediction quality.
  • Visualization helps to interpret results and identify areas for improvement.

Data Collection and Preparation

Data Collection:
In this project, we collected the dataset from a public repository. If you are looking to work on a real-world problem, you can get these kinds of datasets from publicly available repositories such as Kaggle, UCI Machine Learning Repository, or company-specific data. We will provide the dataset in this project so that you can work on the same dataset.

Data Preparation Workflow

  • Load ut the text data in a Pandas Dataframe so that it can be manipulated and analyzed with ease.
  • Correctly Format the text by removing unnecessary characters, punctuation, and noise.
  • Tokenize the text into words/sentences using NLTK by implementing the tokenization methods.
  • Eliminate any stop words such as the, is, etc. to retain only the important words in the text.
  • Stemming or lemmatization is then done to ensure consistency in the usage of words by reducing them to their respective root forms.
  • Vectors are formed out of the processed text using CountVectorizer or TfidfVectorizer.
  • Split the dataset into training and testing sets to prepare for model building.

Code Explanation

STEP 1

Mounting Google Drive

First, mount Google Drive to access the dataset that is stored in the cloud.

from google.colab import drive
drive.mount('/content/drive')

Library Installation

The following code installs the required Python Libraries such as scikit-learn, numpy, pandas, seaborn, matplotilb, etc. They are employed to assist in Data Analytics, Visualization, Machine Learning and Natural language Processing.

!pip install scikit-learn
!pip install numpy
!pip install pandas
!pip install seaborn
!pip install matplotlib
!pip install collections
!pip install nltk
!pip install sklearn
!pip install warnings

Import Library and Environment Configuration

The following code imports data manipulation (NumPy, Pandas), plotting (Matplotlib, Seaborn), and NLP (NLTK) libraries. It also initializes machine learning libraries (scikit-learn), downloads the necessary NLTK resources, turns off the warning messages, and allows for the plotting in the notebook.

import pickle
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer, LancasterStemmer
from nltk.tokenize import sent_tokenize, word_tokenize, RegexpTokenizer
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
import pickle
from sklearn.model_selection import train_test_split
import warnings
# Download necessary NLTK resources
import nltk
nltk.download('stopwords')
nltk.download('punkt')
# Suppress FutureWarnings
warnings.simplefilter(action='ignore', category=FutureWarning)
# Enable inline plotting for Jupyter Notebooks
%matplotlib inline

STEP 2

Loading Data and Checking Dimensions:

This code loads the CSV file. After loading the dataset it prints the dataset’s shape to check the number of rows and columns. The %time magic command in the notebook records the time taken to perform the task.

data = pd.read_csv("/content/drive/MyDrive/New 90 Projects/Project_8/Canva_ review_data.csv")
data.shape

Previewing Data

This block of code displays the first 3 rows of the dataset to have a quick overview of the structure of the dataset.

Code Editor