M-SENA is an open-sourced platform for Multimodal Sentiment Analysis. It aims
to facilitate advanced research by providing flexible toolkits, reliable
benchmarks, and intuitive demonstrations. The platform features a fully modular
video sentiment analysis framework consisting of data management, feature
extraction, model training, and result analysis modules. In this paper, we
first illustrate the overall architecture of the M-SENA platform and then
introduce features of the core modules. Reliable baseline results of different
modality features and MSA benchmarks are also reported. Moreover, we use model
evaluation and analysis tools provided by M-SENA to present intermediate
representation visualization, on-the-fly instance test, and generalization
ability test results. The source code of the platform is publicly available at
this https URL.

M-SENA 是一个开源的平台，旨在提供灵活的工具包、可靠的基准测试和直观的演示，其中包括完全模块化的视频情感分析框架，本文介绍了该平台的总体架构以及核心模块的特性，并报告了不同模态特征和 MSA 基准测试的可靠基线结果，同时使用 M-SENA 提供的模型评估和分析工具来展示中间表示可视化、实例测试以及泛化能力测试结果。

M-SENA：一种多模态情感分析集成平台

M-SENA: An Integrated Platform for Multimodal Sentiment Analysis

Video sentiment analysis as a decision-making process is inherently complex,
involving the fusion of decisions from multiple modalities and the so-caused
cognitive biases. Inspired by recent advances in quantum cognition, we show
that the sentiment judgment from one modality could be incompatible with the
judgment from another, i.e., the order matters and they cannot be jointly
measured to produce a final decision. Thus the cognitive process exhibits
"quantum-like" biases that cannot be captured by classical probability
theories. Accordingly, we propose a fundamentally new, quantum cognitively
motivated fusion strategy for predicting sentiment judgments. In particular, we
formulate utterances as quantum superposition states of positive and negative
sentiment judgments, and uni-modal classifiers as mutually incompatible
observables, on a complex-valued Hilbert space with positive-operator valued
measures. Experiments on two benchmarking datasets illustrate that our model
significantly outperforms various existing decision level and a range of
state-of-the-art content-level fusion approaches. The results also show that
the concept of incompatibility allows effective handling of all combination
patterns, including those extreme cases that are wrongly predicted by all
uni-modal classifiers.

本研究利用量子认知思想提出了一种新的视频情感分析数据融合策略，将话语表示为含有正、负情感判断量子叠加态的复值希尔伯特空间的可共存不可测量观测值，在基准数据集的实验中，该模型较现有决策层和内容层融合方法处理复杂情感信息更具优势，并能有效处理所有组合情况，包括所有单模分类器错误判断的极端情况。