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Table of Contents

• Project Overview
• Objective
• Key Visuals
• Libraries Used
• Key Insights
• Dataset Description
• A Glimpse of the Visual Analysis

📌 Project Overview

This project analyzes public sentiment from social media posts across platforms like Instagram, Twitter, and Facebook using VADER Sentiment Analysis. The goal is to classify posts into Positive, Negative, or Neutral categories and extract insights based on platform, country, and user engagement.

🔍 Objective

• Use the VADER sentiment analyzer from NLTK to classify social media posts into Positive 😀, Neutral 😐, or Negative 😠 sentiment.
• Count and compare the number of posts per platform (Instagram, Twitter, Facebook).
• Analyze sentiment trends per platform and across different countries.
• Identify top engaged users based on retweet counts.
• Generate a word cloud to highlight the most frequently used hashtags in the posts.

📊 Key Visuals

This project features high-quality dashboard visualizations, including:

• 📈 Yearly sentiment trends across platforms
• 🌍 Top 10 countries by engagement and sentiment
• 🗓 Posts distribution over time (monthly/yearly)
• 💬 Platform-wise sentiment comparison
• 🧑‍🤝‍🧑 Top engaged users based on retweets
• ☁️ Word cloud of popular hashtags

🛠️ Libraries Used

• pandas
• numpy
• matplotlib
• seaborn
• nltk
• TextBlob
• vaderSentiment
• wordcloud

📈 Key Insights

• Instagram had the highest number of posts.
• The majority sentiment was Positive 😀, followed by Negative 😠, then Neutral 😐.
• Top engaged user by retweets: CosmosExplorer (80 retweets).
• Frequent hashtags: #serenity, #excitement, #gratitude, #nostalgia, etc.
• USA, UK, and Canada showed the highest positive sentiment.

📂 Dataset Description

The dataset contains public posts collected from Instagram, Twitter, and Facebook with the following fields:

• Text: The content of the social media post

• Platform: Source of the post (Twitter, Instagram, Facebook)

• User: Username who posted

• Timestamp: Date and time of the post

• Country: Country of origin

• Hashtags: Any hashtags used in the post

• Likes, Retweets: Engagement metrics

• VADER_Sentiment: Final sentiment classification (Positive, Neutral, Negative)

  🔗 Dataset Source

The dataset used in this project was sourced from Kaggle:

Social Media Sentiments Analysis Dataset

👀 A Glimpse of the Visual Analysis

Below are a few selected plots that highlight key insights from the dataset.

😊 Thank you for your attention to this project! If you have any questions or feedback, feel free to reach out.