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

arXiv: https://arxiv.org/abs/2404.13778 · HTML: https://mlsanity.com/paper/2404.13778/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 21 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Stimuli-Aware Visual Emotion Analysis](https://mlsanity.com/paper/2109.01812/) (2021)
- [Emotion Dynamics in Movie Dialogues](https://mlsanity.com/paper/2103.01345/) (2021)
- [EmoCo: Visual Analysis of Emotion Coherence in Presentation Videos](https://mlsanity.com/paper/1907.12918/) (2019)
- [Color-Emotion Associations in Art: Fuzzy Approach](https://mlsanity.com/paper/2311.18518/) (2023)
- [Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children](https://mlsanity.com/paper/2309.00138/) (2023)
- [Towards a Universal Understanding of Color Harmony: Fuzzy Approach](https://mlsanity.com/paper/2310.00791/) (2023)
- [Movie Recommendation System using Sentiment Analysis from Microblogging Data](https://mlsanity.com/paper/1811.10804/) (2018)
- [Group Movie Selection using Multi-channel Emotion Recognition](https://mlsanity.com/paper/2403.12087/) (2024)
- [Movie Recommender System using critic consensus](https://mlsanity.com/paper/2112.11854/) (2021)

## Built on by (most cited first, 1 of 1)

- [Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis](https://mlsanity.com/paper/2408.01838/) (2024)

## Abstract (arXiv)

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.