Suppose you work for a social media marketing company that specializes in promoting brands and products on a popular social media platform. Your team is responsible for analyzing the performance of different types of posts based on categories, such as health, family, food, etc. to help clients optimize their social media strategy and increase their reach and engagement.
They want you to use Python to automatically extract tweets posted from one or more categories, and to clean, analyze and visualize the data. The team will use your analysis to making data-driven recommendations to clients to improve their social media performance. This feature will help the marketing agency deliver tweets on time, within budget, and gain fast results.
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Increase client reach and engagement
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Gain valuable insights that will help improve social media performance
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Achieve their social media goals and provide data-driven recommendations
Your task will be taking on the role of a social media analyst responsible for collecting, cleaning, and analyzing data on a client's social media posts. You will also be responsible for communicating the insights and making data-driven recommendations to clients to improve their social media performance. To do this, you will set up the environment, identify the categories for the post (fitness, tech, family, beauty, etc) process, analyze, and visualize data.
Social media has become a ubiquitous part of modern life, with platforms such as Instagram, Twitter, and Facebook serving as essential communication channels. Social media data sets are vast and complex, making analysis a challenging task for businesses and researchers alike. In this project, we explore a simulated social media, for example Tweets, data set to understand trends in likes across different categories.
To follow along with this project, you should have a basic understanding of Python programming and data analysis concepts. In addition, you may want to use the following packages in your Python environment:
- pandas
- Matplotlib
These packages should already be installed in Coursera's Jupyter Notebook environment, however if you'd like to install additional packages that are not included in this environment or are working off platform you can install additional packages using !pip install packagename within a notebook cell such as:
!pip install pandas!pip install matplotlib
The objective of this project is to analyze tweets (or other social media data) and gain insights into user engagement. We will explore the data set using visualization techniques to understand the distribution of likes across different categories. Finally, we will analyze the data to draw conclusions about the most popular categories and the overall engagement on the platform.