ENHANCING AMAZON ALEXA PRODUCT RECOMMENDATIONS USING CNN AND NLP METHODS
Keywords:
Logistic Regression, Multilayer Perceptron, Artificial Neural Networks, Word Embeddings, Convolutional Neural Networks (CNN), GloVe, Sentiment Prediction, Machine Learning Models, E-commerce Reviews, Text Analytics, Amazon Reviews, Consumer Opinion Analysis.Abstract
Research in this area has recently centered on the development of smart home systems like Amazon Alexa, with a particular emphasis on analyzing user feedback for Echo, Echo Dots, and Firesticks. To determine if customer sentiment is good or negative, the research makes use of machine learning methods such as GloVe, CNN, ANN, and logistic regression. To measure how well these models work, metrics like recall and precision are used. This research shows how useful insights into customer attitudes gained through feedback can improve product development, user happiness, and purchasing decisions. The relevance of strong emotion prediction models in helping businesses improve customer experiences and strategies is highlighted, laying the groundwork for the growth of the rapidly developing smart home technology industry.
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