Sentiment Analysis

Overview

This project aims to detect sentiments in text using machine learning techniques. It leverages Natural Language Processing (NLP) and classification algorithms to classify text into either positive,negative,neutral or irrelevant categories.

Table of Contents

• Introduction
• Features
• Installation
• Dataset
• Preprocessing
• Model Training
• Evaluation
• Results
• Contributing

Introduction

In today's digital age, vast amounts of textual data are generated daily across various platforms such as social media, customer reviews, and news articles. Understanding the sentiment expressed in this data is crucial for businesses, researchers, and individuals to gain insights into public opinion, customer satisfaction, and overall sentiment trends.

The Sentiment Analysis Project is an initiative focused on utilizing machine learning techniques to analyze and classify textual data based on sentiment. By leveraging Natural Language Processing (NLP) algorithms and classification models, this project aims to automatically categorize text into different sentiment classes, such as positive, negative, or neutral.

The core objective of this project is to develop a robust sentiment analysis tool that can accurately interpret the sentiment conveyed in textual data. This tool can be applied to various real-world applications, including social media monitoring, brand sentiment analysis, customer feedback analysis, and market research.

This README provides an overview of the project, including details on the dataset used, instructions for running the code, the directory structure of the repository, contributions from collaborators, and licensing information.

Features

• Utilizes sentiment analysis for text classification.
• Integration with popular machine learning libraries such as TensorFlow and Keras.
• Evaluation metrics for assessing the model's performance.

Installation

Clone the repository:

git clone https://github.com/AmineRaouane/Sentiment.git

Dataset

The project utilizes a curated dataset that is a collection of twwiter(X) posts . The dataset is in twitter_training.csv

Preprocessing

The preprocessing script encode the text column to use it in the modeling part.

Model Training

The model is trained on the preprocessed dataset using the TensorFlow and Keras libraries. Hyperparameters can be adjusted in the Sentiment.ipynb script.

Evaluation

The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into the model's ability to correctly the posts.

Results

Upon training and evaluation, the project provides insights into the model's performance. Results, including confusion matrices and classification reports, are presented in the Sentiment.ipynb script.

Contributing

Contributions to the project are welcome. Feel free to open issues, propose new features, or submit pull requests.