Customer reviews play pivotal roles in consumers’ purchase decisions,
but the sheer volume of text data can be overwhelming. In existing
system, while ensemble methods can enhance performance, the
associated computational complexity and resource intensiveness
should be carefully considered, and appropriate measures should be
taken to address these challenges in the context of Customer Review
Summarization. This study introduces a Particle Swarm Optimization
(PSO) based optimized architecture for a Bidirectional Convolutional
Recurrent Neural Network (BiCRNN) with a group-wise enhancement
mechanism tailored for Customer Review Summarization and named
as OBiCRNN. This model is designed for the perspective of customer
review summarization, aiming to effectively capture sentiments and
generate concise summaries. The integration of PSO optimizes the
network parameters, enhancing the learning process. Feature
extraction is done by Modified Principal Component Analysis (MPCA)
which uses correlated feature sets and extracts most informative
features for given datasets. BiCRNN utilizes bidirectional LSTM and
GRU layers for comprehensive context understanding, while the group-
wise enhancement mechanism categorizes sentiment-related features,
amplifying essential sentiments and attenuating less relevant ones.
With this novel approach, the architecture leverages both PSO and
BiCRNN for an advanced framework in customer review
summarization where outcomes demonstrate the effectiveness of the
deep learning (DL) model in producing coherent and informative
summaries, enhancing the accessibility of customer feedback for both
consumers and businesses. The study contributes to the field of natural
language processing (NLP) and customer sentiment analysis, offering
a scalable solution for managing the wealth of information present in
online customer reviews.