A Comparative Study of Hybrid Recommendation Systems for E- commerce Based on Sentiment Analysis and Star Ratings using the weighted Hybrid Approach

Source abstract

In recent times, the use of machine learning and deep learning models in sentiment analysis and star rating prediction has been a focal point of several studies. This research embarked on an indepth examination and comparison of traditional and hybrid models in sentiment analysis and star rating prediction. The traditional models showcased strong capabilities with Random Forest illustrating high accuracy and exceptional classification abilities in sentiment analysis, and Logistic Regression surfacing as a dominant contender in rating predictions. In contrast, hybrid models, characterized by heightened flexibility and tuning capabilities, manifested optimal performance within specific sentiment weight ranges, thus indicating a promising approach to enhancing prediction accuracy and reliability. The project also highlighted significant learning and opportunities for innovation, despite encountering limitations such as computational resource constraints and dataset imbalances. The research implies that a careful amalgamation of various model strengths and feature combinations could potentially pave the way for achieving peak performance in sentiment analysis and rating predictions. Moving forward, future research could delve into exploring new hybrid combinations and further optimization of model parameters to enhance performance. This study sets a promising precedent for the development of more advanced and efficient predictive analytics solutions, thus fostering growth and innovation in the fields of machine learning and data science.