New BRICS Sentiment Analysis
Overview:
Welcome to the New BRICS Sentiment Analysis project! This open-source project focuses on sentiment analysis of text data related to the New BRICS (Brazil, Russia, India, China, South Africa) nations. The goal is to develop a machine learning model capable of understanding and classifying sentiments expressed in text data from various sources.
Table of Contents
Introduction
Features
Getting Started
Prerequisites
Installation
Dataset
Training
Evaluation
Contributing
License
Introduction:
Sentiment analysis is a crucial aspect of understanding public opinion, and this project aims to apply machine learning techniques to analyze sentiments related to the New BRICS nations. By leveraging natural language processing (NLP) and machine learning models, we aim to provide insights into public sentiments expressed in text data.
Features:
Sentiment analysis on text data related to the New BRICS nations. NLP techniques for text preprocessing. Machine learning models for sentiment classification. Sentiment Classification: The core functionality of this project is the sentiment classification system. It classifies input text into positive, negative, or neutral sentiments.
Prerequisites: install the necesary libraies such as: 1.Numpy 2.Pandas 3.Sklearn 4.XGBoost 5.Sci-py 6.Matplotlib
Installation Steps Clone the repository: git clone https://github.com/yourusername/sentiment-analysis-project.git Navigate to the project directory: cd sentiment-analysis-project Install dependencies: pip install -r requirements.txt
Dataset:
The dataset used for training and evaluation is available in the 'data' directory. Please refer to the dataset documentation https://www.kaggle.com/datasets/syedali110/6-new-brics-members-sentiment-analysis for more details. It has four coulmns namely text display,unnamed,labels and likes.It has 571 rows and 4 columns.
License:
This project is licensed under the MIT License.
