  Sentiment analysis, a vital component in natural language processing, plays a
crucial role in understanding the underlying emotions and opinions expressed in
textual data. In this paper, we propose an innovative ensemble approach for
sentiment analysis for finding fake reviews that amalgamate the predictive
capabilities of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and
Decision Tree classifiers. Our ensemble architecture strategically combines
these diverse models to capitalize on their strengths while mitigating inherent
weaknesses, thereby achieving superior accuracy and robustness in fake review
prediction. By combining all the models of our classifiers, the predictive
performance is boosted and it also fosters adaptability to varied linguistic
patterns and nuances present in real-world datasets. The metrics accounted for
on fake reviews demonstrate the efficacy and competitiveness of the proposed
ensemble method against traditional single-model approaches. Our findings
underscore the potential of ensemble techniques in advancing the
state-of-the-art in finding fake reviews using hybrid algorithms, with
implications for various applications in different social media and e-platforms
to find the best reviews and neglect the fake ones, eliminating puffery and
bluffs.
