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.

通过分析电影的三个不同渠道 (文本、音频和图像) 中的情感，利用 Jaccard 相似性指数匹配参与者的情感偏好与电影选择，并使用模糊推理技术确定群体共识，从而提供群体电影建议。