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A virtual internship project to analyze a client's (Social_Buzz) text data to uncover top 5 categories for increased engagement and identification of partners. Pandas, Matplotlib, and Seaborn

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Text-Analysis-Accenture-North-America-

A virtual internship project to analyze a client's (Social_Buzz) text data to uncover top 5 categories for increased engagement and identification of partners

Project Recap

Social Buzz a rapidly growing social media Unicorn occupying a niche where content, takes center stage. Accenture contracted to help it navigate to the next level by analyzing its trove of text data to uncover top 5 content categories.

Business Problem

Social Buzz 500 million active users generate over 100,000 pieces of content daily, a massive 36.5 million pieces annually! The company has not leveraged on the massive data set to enhance activity on the site and attract partners, a focus for the management in the current growth phase, which includes an Initial Public Offering (IPO).

Analysis Process

The project lead assembles analytics team, explains deliverables, and plans a meeting with the client to learn about their business and needs. Next, the analytics team selects necessary datasets for the business need using the company data model, understands the data, cleans and models the data, analyzes, and reports insights.

Key Findings

  • 16 unique content categories identified
  • Animal posts had most reactions at 1897 in the year
  • January was the busiest month with 781 posts
  • Other categories in the top 5: science, healthy eating, technology, and food

Insights

  • Healthy eating and food among the top 5; indicating a health-conscious active users for Social Buzz.
  • Animals, science, and technology the other categories in the top 5; indicating a predilection for "realist" topics for Social Buzz active users.
  • Recommend further analysis incorporating user labels to uncover segments for brand partnerships in healthy eating, pets, and science.

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A virtual internship project to analyze a client's (Social_Buzz) text data to uncover top 5 categories for increased engagement and identification of partners. Pandas, Matplotlib, and Seaborn

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