"The golden period of television has moved to the

screen in our hands, as streaming in the era of OTT(over-thetop) platforms. However, among OTT users, disengagement and

platform churn are becoming more frequent. This study

examines sentiment analysis in the context of OTT content

through a comparative analysis of machine learning (ML) and

deep learning (DL) models on user reviews from the Google Play

Store and Apple App Store. We analyzed 56,351 user reviews

from ten popular OTT apps, classifying them as positive

(21,446), neutral (19,120), and negative (15,785). Preprocessed

and feature-extracted data was fed to both ML models (Logistic

Regression, XGBoost, etc.) and DL models (BiLSTM, LSTM,

CNN). All of these are capable of extracting textual

characteristics and insights from datasets and are also used to

capture complex sentiment nuances in user reviews. BiLSTM, a

deep learning algorithm, surpassed all other models, achieving

an astounding 92% accuracy compared to Logistic regression,

which achieved 66.62% accuracy and was the best-performing

machine learning model in capturing user sentiment."