Improving Digital Marketing Using Sentiment Analysis with Deep LSTM

With As digital channels continue to grow, digital marketing has become a crucial area for businesses. Customers share their experiences with products on social media and e-commerce platforms, providing businesses with valuable feedback. Sentiment analysis techniques are used to analyze customer feedback and improve business decisions. Deep learning techniques, such as Long Short-Term Memory (LSTM), have the potential to extract knowledge from large volumes of data with greater accuracy than manual approaches. In this study, we propose using Deep LSTM to enhance the accuracy of sentiment analysis. Our simulation results show that the proposed model improves upon conventional schemes in terms of accuracy, precision, recall, and F-measure. The proposed model achieved an accuracy rate of over 90%, which is significantly higher than the accuracy rate achieved by other sentiment analysis models. Additionally, the proposed model outperformed other state-of-the-art sentiment analysis techniques in our empirical evaluation using a large dataset. Furthermore, we tested the proposed model in a real-world scenario, where it was used to analyze customer sentiment toward a newly launched product. The proposed model accurately identified positive and negative sentiments expressed by customers toward the product. The marketing team used this information to make informed decisions regarding product improvements and marketing strategies, demonstrating the practical applications of the proposed model. Our study highlights the effectiveness of deep learning techniques, specifically deep LSTM, in improving the accuracy and reliability of sentiment analysis. Our findings have important implications for businesses seeking to leverage customer feedback to improve their products and services.