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Learn sentiment workflow and RNN/LSTM concepts, then implement LSTM sentiment modeling in Keras, the source names voice-of-customer reviews/surveys/social material as marketing/service applications
Abhishek Kumar · Udemy
Learn sentiment workflow and RNN/LSTM concepts, then implement LSTM sentiment modeling in Keras. The source names voice-of-customer reviews/surveys/social material as marketing/service applications
Selected published requirements: “Basic Python programming”. See the provider page for the full requirements and current tool terms
Recorded Udemy course advertised with a public course preview. Exact lesson count may be unknown. Purchase or organization-subscription terms and current availability require checking
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Dr. Mohammad Nauman
Implement a concise Keras/Python sentiment engine, develop the case-study model and extend it with CNNs, source targets understanding opinions about company products/services
Yaswanth Sai Palaghat
Use TextBlob to classify text sentiment and build a Flask interface, public description explicitly frames customer reviews/social opinions as applications. A technical NLP course transfers to feedback monitoring
O.P. Jindal Global University
Marketing text-mining curriculum applies sentiment, topic modeling, NLP, named-entity recognition, classification, topic clustering and predictive analysis to customer reviews, social posts, feedback and news. Named uses include…