Sentiment Analysis in R

This project implements sentiment analysis using a Naive Bayes classifier in R. The application is built using the Shiny framework for interactive web applications.

Table of Contents

• Introduction
• Getting Started
• Features
• Code Overview
• Contributing

Introduction

Sentiment analysis is a natural language processing (NLP) task that involves determining the sentiment expressed in a piece of text, such as positive, negative, or neutral. This project focuses on sentiment analysis from textual data using a Naive Bayes classifier.

Getting Started

To run the project locally, follow these steps:

1. Clone the Repository:

   git clone https://github.com/Adhithya-Laxman/Sentimental-Analysis-in-R.git
   cd Sentimental-Analysis-in-R

2. Install Dependencies: Ensure you have the required R libraries installed. You can install them using:

   install.packages(c("shiny", "ggplot2", "caret", "tm", "e1071", "tidytext"))

3. Run the Application: Open the R script app.R in RStudio or your preferred R environment and run the application.

4. Use the Application: Open your web browser and navigate to http://127.0.0.1:port, where port is the port specified in your R environment.

Features

• CSV Input:

  • Users can upload a CSV file containing text data for sentiment analysis.

• Remove Stopwords:

  • An option to remove stopwords from the text data for more accurate analysis.

• Run Analysis:

  • Perform sentiment analysis on the uploaded data with the click of a button.

• Emotion Plot:

  • Visualize the distribution of sentiments in the data through an interactive plot.

• Confusion Matrix:

  • Evaluate the performance of the sentiment analysis with a confusion matrix.

• Metrics:

  • Display accuracy, sensitivity, specificity, precision, and F1 score as evaluation metrics.

• Output Table:

  • View a table containing sampled lines and their predicted sentiments.

Code Overview

The code is organized into the following sections:

• UI Definition:

  • The Shiny UI is defined in the ui variable, specifying the layout and components of the web application.

• Server Logic:

  • The server logic is defined in the server function, including data loading, preprocessing, model training, and result visualization.

• Sentiment Analysis:

  • The Naive Bayes classifier is trained on the provided data, and sentiment analysis is performed on a test dataset.

• Visualization:

  • The project includes visualization components such as the emotion plot, confusion matrix, and evaluation metrics.

Contributing

Contributions are welcome! If you have ideas for improvements or new features, feel free to open an issue or submit a pull request.