YouTube Ad View Sentiment Analysis using Deep Learning and Machine Learning

TL;DR

This paper explores sentiment analysis of YouTube ad views by applying and comparing various deep learning and machine learning models to understand viewer reactions.

Contribution

It introduces a comparative analysis of multiple ML and DL algorithms for YouTube ad sentiment prediction, highlighting their effectiveness.

Findings

ANN achieved the highest accuracy among models

Support Vector Machine showed strong performance in sentiment classification

Random Forest provided reliable results across datasets

Abstract

Sentiment Analysis is currently a vital area of research. With the advancement in the use of the internet, the creation of social media, websites, blogs, opinions, ratings, etc. has increased rapidly. People express their feedback and emotions on social media posts in the form of likes, dislikes, comments, etc. The rapid growth in the volume of viewer-generated or user-generated data or content on YouTube has led to an increase in YouTube sentiment analysis. Due to this, analyzing the public reactions has become an essential need for information extraction and data visualization in the technical domain. This research predicts YouTube Ad view sentiments using Deep Learning and Machine Learning algorithms like Linear Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). Finally, a comparative analysis is done based on experimental results acquired from different models.