Sentiment Analysis of Consumer Web Reviews Based on CNN-BiLSTM

In the evolving e-commerce landscape, numerous subjective evaluations of products are surfacing on diverse online platforms. These user-generated reviews encapsulate sentiments towards various product attributes, aiding sellers in identifying the strengths and weaknesses of their offerings. Consequently, this feedback facilitates product enhancement and informs prospective buyers, enabling more judged purchasing decisions. Leveraging sentiment analysis on voluminous review datasets can elucidate customer attitudes towards specific goods or services, thus providing businesses with insights into customer emotions. This study introduces a hybrid Convolutional Neural Network combined with Bidirectional Long Short-Term Memory (CNN-BiLSTM) model designed for sentiment analysis of consumer reviews. This approach exhibits robust scalability, sustaining high-performance metrics such as accuracy, recall and F1 scores above 92%, even with the addition of fresh datasets. The proposed CNN-BiLSTM model surpasses conventional CNN and RNN models by integrating the superior predictive power of the BiLSTM component, which is adept at capturing long-range dependencies and context. Contrasted with a standalone BiLSTM model, our proposed architecture leverages the strengths of both technologies: the CNN layer for efficient feature dimensionality reduction and the BiLSTM layer for extracting temporal and contextual information. This synergy enhances the extraction of pertinent features from online consumer reviews, thereby boosting prediction accuracy and operational efficiency.