In this data science portfolio project, we conducted a comprehensive sentiment analysis on LinkedIn app reviews using Python programming. The project began with the collection and exploration of a diverse dataset, followed by meticulous labeling of sentiments using TextBlob. Analyzing the distribution of ratings and exploring the relationship between sentiments and ratings provided valuable insights into user perceptions. Leveraging text analysis techniques, we identified common themes and words within each sentiment category, shedding light on user sentiments.

This project serves as a robust demonstration of applying data science methodologies to extract actionable insights from user feedback, offering a strategic advantage for app developers and businesses aiming to enhance user experience on the LinkedIn platform.

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