I am working on a pet project to implement a real-time sentiment analysis system for analyzing customer reviews. It leverages Kafka for data ingestion, Spark Structured Streaming (SSS) for real-time processing, and Vertex AI for sentiment analysis and potential action triggers.
*Features* - Real-time processing of customer reviews using SSS. - Sentiment analysis using pre-assigned labels or Vertex AI <https://cloud.google.com/vertex-ai?hl=en>models. - Integration with Vertex AI for model deployment and prediction serving. - Potential actions based on sentiment analysis results (e.g., notifications, database updates). *Tech stack* - Kafka: Stream processing platform for data ingestion. - SSS for real-time data processing on incoming messages with cleansing - Vertex AI: Machine learning platform for model training I have created sample Json data with relevant attributes for product review as shown below { "rowkey": "7de43681-0e4a-45cb-ad40-5f14f5678333", "product_id": "product-id-1616", "timereported": "2024-02-21T08:46:40", "description": "Easy to use and setup, perfect for beginners.", "price": GBP507, "sentiment": negative, "product_category": "Electronics", "customer_id": "customer4", "location": "UK", "rating": 6, "review_text": "Sleek and modern design, but lacking some features.", "user_feedback": "Negative", "review_source": "online", "sentiment_confidence": 0.33, "product_features": "user-friendly", "timestamp": "", "language": "English" }, I also attached a high level diagram. There is recently a demand for Gemini usage. Your views are appreciated. Thanks Mich Talebzadeh, Dad | Technologist | Solutions Architect | Engineer London United Kingdom view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> https://en.everybodywiki.com/Mich_Talebzadeh *Disclaimer:* I am an architect and not a data scientist. The information provided is correct to the best of my knowledge but of course cannot be guaranteed . It is essential to note that, as with any advice, quote "one test result is worth one-thousand expert opinions (Werner <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".
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