Emotion-Based Media Recommendation System

Emotion is a state of physical arousal that is subjective and private to the individual. Human emotions are divided into fear, anger, surprise, sadness, happiness, etc. This emotional umbrella encompasses a wide range of other emotions, including joy and contempt; they are subtle emotions. When it comes to recognizing emotions and present mental states, facial expressions are crucial (i.e., emotional and spiritual). Detecting these emotions can be difficult at times, as even minor variances might result in distinct displays. To detect emotions and produce good results, neural networks and machine learning were applied. Machine learning algorithms have proven to be very useful in pattern recognition and classification and thus can also be used for mood detection. With the development of digital music technology, it is also necessary to develop a personalized music recommendation system to recommend music to users. Emotions are an essential part of the human psyche. It has many different types, and the main purpose of our proposed system is to distinguish them. For this, our proposed system is based on the proposed structure of video content that is related to human emotions. Recommendation system based on user logs or history. The recommendation technique we use is not fully personalized nor is it considered a point of user content. Therefore, the solution to this problem is to recommend personalized content based on emotional characteristics. According to letter of our proposed system, taking into account aspects such as age, gender and emotional characteristics, our main motivation is to control the problem of accuracy in applications running. Our proposed emotion-based media recommendation system is a novel and innovative approach that has the potential to revolutionize the way we interact with digital media.