Multi-channel Emotion Analysis for Consensus Reaching in Group Movie Recommendation Systems

Watching movies is one of the social activities typically done in groups. Emotion is the most vital factor that affects movie viewers' preferences. So, the emotional aspect of the movie needs to be determined and analyzed for further recommendations. It can be challenging to choose a movie that appeals to the emotions of a diverse group. Reaching an agreement for a group can be difficult due to the various genres and choices. This paper proposes a novel approach to group movie suggestions by examining emotions from three different channels: movie descriptions (text), soundtracks (audio), and posters (image). We employ the Jaccard similarity index to match each participant's emotional preferences to prospective movie choices, followed by a fuzzy inference technique to determine group consensus. We use a weighted integration process for the fusion of emotion scores from diverse data types. Then, group movie recommendation is based on prevailing emotions and viewers' best-loved movies. After determining the recommendations, the group's consensus level is calculated using a fuzzy inference system, taking participants' feedback as input. Participants (n=130) in the survey were provided with different emotion categories and asked to select the emotions best suited for particular movies (n=12). Comparison results between predicted and actual scores demonstrate the efficiency of using emotion detection for this problem (Jaccard similarity index = 0.76). We explored the relationship between induced emotions and movie popularity as an additional experiment, analyzing emotion distribution in 100 popular movies from the TMDB database. Such systems can potentially improve the accuracy of movie recommendation systems and achieve a high level of consensus among participants with diverse preferences.

Group Movie Selection using Multi-channel Emotion Recognition

Social activities often done in groups include watching television or movies. Choosing a film that appeals to the emotional inclinations of a varied group can be tricky. One of the most difficult aspects of making group movie suggestions is achieving agreement among members. At the same time, emotion is the most important component that connects the film and the viewer. Current research proposes a methodology for group movie selection that employs emotional analysis from numerous sources, such as film posters, soundtracks, and text. Our research stands at the intersection of emotion recognition technology in music, text, color images, and group decision-making, providing a practical tool for navigating the complex dynamics of film selection in a group setting. The survey participants were given emotion categories and asked to select the emotions that best suited a particular movie. Preliminary comparison results between real and predicted scores show the effectiveness of using emotion detection for group movie recommendation. Such systems have the potential to enhance movie recommendation systems.