Abstract:
In this era of Internet technology in Bangladesh the online food supply industry was still
flourishing. Most individuals in Bangladesh's city region may easily order their food from
several food suppliers during the epidemic for Covid-19. New consumer comfort doors are
opened by a growing trend in food supply. People try to make meal choices based on ratings
and feedback. The quality of meals is not only ranking. Rating is a mix of menu, delivery
surveillance program and delivery attitude. Users must thus read every food remark.
However, this procedure takes time. Because every remark on different meals is tough to
read. We sought to create an intelligent meal evaluation system for this purpose. For
various online meal delivery applications, we have gathered around 840 Bangla phrases.
In our study we have utilized sentiment analyses and some classification algorithms such
as KNN, Decision Tree, Support Vector Machine (SVM), and deep learning based LSTM
algorithm. We utilized an algorithm to forecast which produced the highest precision and
F1 score.
