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.
翻译：感官分析目前是一个至关重要的研究领域。 随着互联网使用的进步、社交媒体、网站、博客、观点、评级等的创建,社交媒体、网站、博客、意见、评级等的创建迅速增加。人们以喜欢、不喜欢、评论等形式在社交媒体上表达他们的反馈和情感。YouTube上浏览者生成或用户生成的数据或内容数量迅速增加,导致YouTube情绪分析增加。因此,分析公众反应已成为技术领域信息提取和数据可视化的基本需要。这项研究预测了YouTube观点,使用深度学习和机器学习算法,如线性回归(LR)、支持矢(SVM)、决策树(DT)、随机森林(Randroand Form)和人工神经网络(ANN)。最后,根据从不同模型获得的实验结果进行了比较分析。
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