It’s crucial for companies to regularly evaluate their users’ likes and reposts on social media because it provides valuable insights into customer preferences, interests, and behaviors.
By monitoring these metrics, companies can gain a better understanding of their target audience and tailor their marketing strategies accordingly. It helps them identify what content resonates well with their followers and enables them to create more engaging and relevant tweets in the future.
Moreover, analyzing likes and reposts allows companies to track the success of their campaigns and initiatives, determining which posts generate higher engagement rates or drive more traffic to their website.
This data-driven approach empowers companies to refine their social media strategies, optimize content creation efforts, and ultimately build stronger relationships with customers by consistently delivering compelling and genuinely interesting content that is aligned with user preferences on the platform.
Graph analysis in Python is a cool way to study and analyze relationships between different entities using graphs. With Python, you have some awesome libraries like NetworkX that make it super easy. You can create graphs with nodes and edges representing different things, and then use these graphs to find patterns or answer questions.
For example, if you want to analyze social networks or study how users evaluate your new products or innovations. With graph analysis in Python, you can measure centrality, detect communities within the network, perform link prediction, and much more.
In this post I will share with you Python code with analysis of customer likes and reposts on Twitter of one very famous brand – Huawei.