TAMU-Sentiment

Table of Contents

• Introduction
• Description
• Features
• Demo
• Usage
• Contributors
• Project Status
• Roadmap
• Additional Resources

Introduction

Welcome to TAMU-Sentiment! Our project aims to analyze and understand the sentiment of the student population at Texas A&M University. By harnessing the power of sentiment analysis, web scraping, and other data science techniques, we seek to capture and interpret the thoughts and beliefs expressed by Texas A&M students online.

Description

TAMU-Sentiment utilizes cutting-edge techniques in sentiment analysis to assess and interpret the emotional tone of student's across Texas A&M. The steps in our approach include:

• Web Scraping: Extracting Relevent data from online sources such as Reddit, Twitter, and Instagram.
• Data Munging: Transforming and cleaning raw data in a format so that it will be more suitable for analysis. This step includes, but is not limited to, data cleaning, data transformation, data integration, data formating, and data filtering.
• Sentiment Analysis: Applying natural language processing (NLP) algorithms to determine the sentiment behind our collected data.
• Data Visualization and Web Development: Developing a website so that users all around Texas A&M and the world can observe the general sentiment of the Texas A&M student cohort.

Features

• Social media scraping
• Text vectorization
• Automatic clustering
• Event label generation
• Sentiment analysis

Demo

Usage

Contributors

Daniel Trinh Zanir Pirani

Project Status

⚠️Still in Progress⚠️

Roadmap

Additional Resources