{"id":24306729,"url":"https://github.com/lalitdotdev/social-sentiment-analysis","last_synced_at":"2026-04-22T13:32:06.354Z","repository":{"id":208978822,"uuid":"722935432","full_name":"lalitdotdev/social-sentiment-analysis","owner":"lalitdotdev","description":"This repository explores sentiment and social network analysis in the context of social media platforms. 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Leveraging NLP techniques, including traditional ML and BERT models, it conducts sentiment analysis on a dataset of 1.6 million Twitter tweets.\n\n\n# Analysis of Some Aspects of Social Networking\n\n## Overview\n\nWelcome to the \"Analysis of Some Aspects of Social Networking\" project! This repository hosts an in-depth exploration of sentiment analysis and social network analysis within the context of natural language processing and a part of my final year project. Dive into the intricate world of human expression on social media platforms and uncover valuable insights from user interactions.\n\n## Table of Contents\n\n1. [Introduction](#introduction)\n2. [Features](#features)\n3. [Getting Started](#getting-started)\n   - [Installation](#installation)\n   - [Usage](#usage)\n4. [Data Preprocessing](#data-preprocessing)\n5. [Sentiment Analysis](#sentiment-analysis)\n6. [Social Network Analysis](#social-network-analysis)\n7. [Results](#results)\n8. [Evaluation](#evaluation)\n9. [Future Improvements](#future-improvements)\n10. [Challenges](#challenges)\n11. [Contributing](#contributing)\n12. [License](#license)\n\n## Introduction\n\nHuman language processing involves two essential components: understanding and generation. In the vast landscape of natural language processing, this project focuses on sentiment analysis and social network analysis. Sentiment analysis evaluates data as positive, negative, or neutral, while social network analysis unveils patterns in user interactions and network structures.\n\n## Features\n\n- **Sentiment Analysis:** Explore the sentiment expressed in textual content using state-of-the-art machine learning and deep learning models.\n- **Social Network Analysis:** Uncover patterns in user interactions, influential nodes, and network structures through graph-based representations and visualizations.\n- **Data Preprocessing:** Prepare massive Twitter datasets for analysis through meticulous preprocessing techniques.\n\n## Getting Started\n\n### Installation\n\nTo get started, clone the repository and install the required dependencies.\n\n```\ngit clone https://github.com/your-username/social-network-analysis.git\ncd social-network-analysis\npip install -r requirements.txt\n```\n\n### Usage\n\nRun sentiment analysis and social network analysis scripts to explore the project functionalities.\n\n```\npython sentiment_analysis.py\npython social_network_analysis.py\n```\n\n## Data Preprocessing\n\nThe first phase involves preprocessing a substantial Twitter dataset containing 1.6 million tweets. The data undergoes transformations, including lowercasing, URL and punctuation removal, and stemming, to prepare it for sentiment analysis.\n\n## Sentiment Analysis\n\nState-of-the-art machine learning models like Naive Bayes and advanced deep learning models such as BERT are employed for sentiment analysis. Evaluation metrics, including accuracy, precision, recall, and F1-score, provide a comprehensive understanding of model performance.\n\n## Social Network Analysis\n\nGraph-based representations and visualizations are utilized to depict network dynamics, fostering a deeper comprehension of relationships within the social graph. Explore influential nodes, user interactions, and overall network structures.\n\n## Results\n\nThe project yields detailed insights and visualizations showcasing sentiment distribution and social network patterns. These results provide valuable information for understanding user behavior and community dynamics.\n\n## Evaluation\n\nEvaluate the performance of sentiment analysis models using standard metrics, including accuracy, precision, recall, and F1-score. Confusion matrices and classification reports enhance interpretability and provide a comprehensive view of model performance.\n\n## Future Improvements\n\nAs the project evolves, consider the following future improvements:\n\n- Incorporate advanced deep learning models, such as BERT, for more nuanced sentiment analysis.\n- Explore cutting-edge machine learning techniques for social network analysis.\n\n## Challenges\n\nAddressing challenges such as contextual nuances in sentiment analysis and diverse emotional expressions in social network analysis is imperative. Standardizing rules for expressing feelings across platforms remains a challenge due to varied communication styles.\n\n## Contributing\n\nFeel free to contribute to this project! Check out our [contribution guidelines](CONTRIBUTING.md) for more information on how to get involved.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flalitdotdev%2Fsocial-sentiment-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flalitdotdev%2Fsocial-sentiment-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flalitdotdev%2Fsocial-sentiment-analysis/lists"}