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Explore Dutch Social Media trends with analyses on influencer behavior, geographical insights, daily activity patterns, sentiment trends, and prevalent topics using Python.

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Dutch Social Media Analysis 📊🇳🇱

Welcome to the Dutch Social Media Analysis project! 🚀 This analysis delves into the Dutch Social Media Collection from Kaggle, featuring 10 files with diverse tweets from users within the Netherlands or tweeting in Dutch.

Key Objectives

  1. Identifying Influencers: Discover the most active users on the platform and analyze their posting behavior.
  2. Handling Missing Values: Address the challenge of missing data for accurate analyses.
  3. Geographical Insight: Visualize the regions of the top 10 influencers using a pie chart.
  4. Daily Activity Analysis: Understand the daily posting habits of influencers through a stacked bar chart.
  5. Sentiment Analysis: Explore sentiment scores across all users over time, identifying potential correlations with notable events.
  6. Day-of-the-Week Analysis: Investigate patterns in sentiment scores throughout the week.
  7. Word Cloud Analysis: Generate a word cloud to highlight the prevalent topics, with a particular focus on the dominant theme.

Highlights

  • 🌐 Geographical Insights: Dive into the regions of the most active Twitter users.
  • 📈 Daily Habits: Visualize the daily posting habits of influencers with a stacked bar chart.
  • 🌐 Sentiment Trends: Explore sentiment score changes over time for intriguing insights.
  • 📅 Day-of-the-Week Patterns: Uncover patterns in sentiment scores throughout the week.
  • ☁️ Word Cloud Magic: Identify prevalent topics, with Covid-19 emerging as a dominant theme.

Sources

https://www.freecodecamp.org/ https://stackoverflow.com/ https://www.w3schools.com/ https://pandas.pydata.org/docs/index.html

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Explore Dutch Social Media trends with analyses on influencer behavior, geographical insights, daily activity patterns, sentiment trends, and prevalent topics using Python.

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