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。

群组电影推荐系统中基于多通道情感分析的共识达成

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

Keywords play a crucial role in bridging the gap between human understanding
and machine processing of textual data. They are essential to data enrichment
because they form the basis for detailed annotations that provide a more
insightful and in-depth view of the underlying data. Keyword/domain driven term
extraction is a pivotal task in natural language processing, facilitating
information retrieval, document summarization, and content categorization. This
review focuses on keyword extraction methods, emphasizing the use of three
major Large Language Models(LLMs): Llama2-7B, GPT-3.5, and Falcon-7B. We
employed a custom Python package to interface with these LLMs, simplifying
keyword extraction. Our study, utilizing the Inspec and PubMed datasets,
evaluates the performance of these models. The Jaccard similarity index was
used for assessment, yielding scores of 0.64 (Inspec) and 0.21 (PubMed) for
GPT-3.5, 0.40 and 0.17 for Llama2-7B, and 0.23 and 0.12 for Falcon-7B. This
paper underlines the role of prompt engineering in LLMs for better keyword
extraction and discusses the impact of hallucination in LLMs on result
evaluation. It also sheds light on the challenges in using LLMs for keyword
extraction, including model complexity, resource demands, and optimization
techniques.

关键词在文本数据的人机处理中起着至关重要的作用，关键词 / 领域驱动的术语提取是自然语言处理中的一个关键任务，本文评估了使用大型语言模型（LLMs）进行关键词提取的性能，并讨论了提示工程和幻觉在结果评估中的影响，同时指出了使用 LLMs 进行关键词提取的挑战。