Leveraging Customer Sentiment Analysis with Deep Learning for Business Growth Forecasting and Product Flaw Identification

This study harnesses Convolutional Neural Networks (CNNs) alongside advanced Deep Learning models such as Long Short-Term Memory (LSTM), Bidirectional LSTM (bi-LSTM), and ResNet, incorporating Time Series Analysis and Word2Vec embeddings for sophisticated customer sentiment analysis within business contexts. We introduce a novel CNN-based framework that integrates these methodologies to simultaneously forecast business growth and identify product flaws, utilizing large-scale datasets comprising customer reviews, feedback, and social media data. By capturing hidden patterns and nuances within text data, our framework transcends traditional sentiment analysis, providing actionable insights for informed decision-making. Through rigorous evaluation, including the application of Time Series Analysis for predicting future sales trends, we demonstrate the effectiveness of our framework in facilitating customer acquisition and pinpointing areas for product improvement. These findings underscore its potential to optimize market performance and drive product development strategies in dynamic business landscapes.