Deep Learning-Based Sentiment Analysis for Digital Market Intelligence through Comparative Evaluation of BiLSTM and CNN Models

Abstract

The rapid growth of digital communication platforms has generated vast amounts of textual data containing valuable insights into public sentiment toward products, services, and digital assets. Accurately analyzing this data is crucial for understanding consumer behavior and market trends in digital marketing and cryptocurrency ecosystems. This study presents a comparative analysis of two deep learning architectures, Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN), for sentiment classification using textual data. Both models were trained for twenty epochs without early stopping to evaluate their full learning potential. The experimental results revealed that the BiLSTM model achieved superior performance with an accuracy of 73.82% and an F1-score of 72.40%, while the CNN model obtained 65.10% accuracy and an F1-score of 64.28%. The BiLSTM demonstrated a higher capability to capture sequential dependencies and contextual semantics through its bidirectional processing, whereas the CNN primarily relied on local feature extraction, limiting its contextual understanding. Class-wise analysis showed that BiLSTM performed strongly in identifying neutral and positive sentiments but struggled with negative sentiment detection due to data imbalance and linguistic ambiguity. These findings highlight BiLSTM’s robustness and suitability for sentiment analysis in digital market applications. The study emphasizes the importance of context-aware deep learning models in supporting data-driven marketing strategies, customer sentiment monitoring, and digital asset analysis. Future research is recommended to integrate transformer-based architectures such as BERT or RoBERTa to further enhance contextual comprehension and improve overall classification performance.