Novel Customer Review Analysis System Based On Balanced Deep Review And Rating Differences In User Preference

Abstract

The swift expansion of online ecommerce
platforms and mobile applications has
made it simpler to collect vast volumes of data,
offering insightful information about customer
behavior. Helping consumers make decisions
about what to buy now requires analyzing user
reviews. In the suggested system, we present a
solution by integrating a CNN (Convolutional
Neural Network) model for review
categorization with NLP (Natural Language
Processing) approaches. To enhance its
comprehension of the subtleties of review
content, the model integrates word embedding,
tokenization, and text preprocessing approaches.
The CNN-based architecture greatly increases
prediction accuracy and processing efficiency by
enhancing the capacity to identify key patterns
and correlations in the data. This method
provides a more accurate and scalable model for
review analysis, hence overcoming the
shortcomings of earlier approaches. It can be
readily modified to manage varied textual
material and large-scale datasets. The
suggested system outperforms the current
methods in terms of classification, according to
experimental evaluation. The approach
increases decision-making confidence in ecommerce
platforms and predicts useful
evaluations by concentrating on important
patterns and correlations in the text data.