Efficient Method for Video Sentiment Analysis

Nowadays, the acquisition of deep knowledge is carried out in many fields like tracking objects from image/video, measuring position, acquiring textual and image content, visual value detection, and recognizing hand gestures. Various deep learning models are available based on classification and regression like Convolutional Neural Networks (CNN), decision trees, linear regression which comes under supervised and Self Organizing Map (SOM), Boltzmann Machines, and Autoencoders which comes under unsupervised models based on clustering. For image classification, one of the well-performing model is CNN. It provides good performance when compared to other deep learning models. In this paper, we propose an efficient method that will analyze sentiment from video. This is carried out by using Convolutional Neural Networks (CNN). The findings are also compared with various well-known deep learning approaches, and the results obtained by the proposed method are higher when compared to existing ones. The proposed methodology gives an accuracy level of 92%. The proposed methodology can be used in various applications such as audio/video data sentiment analysis, monitoring social media, and customer feedback analysis, in the education field to draw student’s opinions.