Media — User Feedback Analysis and Sentiment Extraction Pipeline

Overview

The User Feedback Analysis and Sentiment Extraction Pipeline is designed to streamline the collection and analysis of user feedback across various channels, including surveys and social media platforms. The primary purpose of this DAG is to gather user insights efficiently, enabling media organizations to enhance their offerings based on real user experiences. The architecture consists of an ingestion pipeline that pulls data from multiple sources, ensuring a comprehensive view of user sentiment

The User Feedback Analysis and Sentiment Extraction Pipeline is designed to streamline the collection and analysis of user feedback across various channels, including surveys and social media platforms. The primary purpose of this DAG is to gather user insights efficiently, enabling media organizations to enhance their offerings based on real user experiences. The architecture consists of an ingestion pipeline that pulls data from multiple sources, ensuring a comprehensive view of user sentiments. The first step involves collecting feedback from designated input sources such as survey responses, social media comments, and user reviews. Following ingestion, the data undergoes preprocessing to clean and normalize it for analysis. Next, sentiment analysis algorithms are applied to classify feedback into positive, negative, or neutral sentiments, while thematic analysis identifies recurring topics and concerns raised by users. The processed data is then aggregated and visualized in reports that highlight key findings and trends. Outputs include detailed sentiment reports, thematic summaries, and actionable insights shared with relevant teams for further action. Monitoring mechanisms are established to track the impact of implemented changes, with KPIs such as user satisfaction scores and feedback response rates. This pipeline ultimately provides significant business value by enabling media companies to respond proactively to user feedback, fostering improved customer satisfaction and loyalty.

Part of the Data & Model Catalog solution for the Media industry.

Use cases

• Enhances user satisfaction through responsive feedback mechanisms
• Drives product improvements based on real user insights
• Increases engagement by addressing user concerns promptly
• Facilitates data-driven decision-making across teams
• Strengthens brand loyalty through proactive user interaction