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PROJECT


Sentiment Analysis Using Multinomial Logistic Regression

Learn to create a classifier for sentiment analysis using multinomial logistic regression.

Sentiment Analysis Using Multinomial Logistic Regression

You will learn to:

Load and preprocess the Twitter Tweets Sentiment Dataset.

Implement a multinomial logistic regression classifier from the scratch for sentiment analysis.

Train and test the model for predicting the sentiment of a given text.

Evaluate the model and display performance metrics using the scikit-learn and Matplotlib libraries.

Skills

Natural Language Processing

Machine Learning

Prerequisites

Intermediate knowledge of Python

Familiarity with machine learning models

Basic understanding of NLP concepts

Basic understanding of supervised learning

Technologies

Pandas

Python

seaborn

Matplotlib

Scikit-learn

Project Description

Multinomial logistic regression is a statistical method used to analyze the relationship between multiple categorical dependent variables and a set of independent variables. It extends the binary logistic regression model to handle three or more categories. The model predicts the probabilities of each category based on the independent variables by estimating coefficients for each category. It employs a softmax function to convert the linear combinations of variables and coefficients into probabilities, allowing for assigning observations to the category with the highest probability. Multinomial logistic regression has applications in various fields and helps understand the factors influencing categorical outcomes and make predictions about different categories.

In this project, we'll build a multiclass classifier from scratch for sentiment analysis using multinomial logistic regression with the Twitter Tweets Sentiment Dataset. We'll preprocess the data by removing the punctuation and converting the tweets into a bag of words. We'll then build a vocabulary based on the most frequent words in the dataset and convert the tweets into feature vectors by using the CountVectorizer function from the scikit-learn library. Subsequently, we'll split the dataset into training and testing subsets with stratified sampling and then we'll implement the multinomial logistic regression classifier. Finally, we'll train and evaluate the model using the training and testing subsets, compute evaluation metrics, and display a confusion matrix and a classification report.

Project Tasks

1

Get Started

Task 0: Introduction

Task 1: Import Libraries

2

Data Preprocessing

Task 2: Load the Dataset

Task 3: Remove Punctuation from Tweets

Task 4: Split Tweets into a Bag of Words

Task 5: Create a Vocabulary and Remove Stop Words

Task 6: Create Feature Vectors

Task 7: Map and Extract the Sentiment Column

Task 8: Split the Dataset into Training and Test Sets

3

Implementing Multinomial Logistic Regression Classifier

Task 9: Define the Weights Initialization Function

Task 10: Define One-Hot Encoding Function

Task 11: Define the Softmax Function

Task 12: Define the Gradient Descent Function

Task 13: Define the Training Function

Task 14: Define the Prediction Function

4

Training, Testing, and Evaluating the Model

Task 15: Train the Model

Task 16: Test the Model

Task 17: Generate the Confusion Matrix and Classification Report

Congratulations!