Presently, large recordings like audio and video are uploaded each day on the public networks particularly Facebook, YouTube, etc. Such postings generate unlimited data via the Internet. Over the next few decades adapting and dealing with data mining to obtain relevant details from social media is considered as a difficult task. To tackle these challenges, a novel text-audio-video consistency-driven multimodal sentiment analysis method is proposed in this paper. This proposed system examines the correlation among the text, audio and video followed by multimodal sentiment analysis. A different set of features are extracted and then the extracted features are chosen optimally by employing a new hybrid grass bee optimization algorithm (HGBEE) thus obtaining a feature set containing an optimal value for better precision and low computational time. Then an utterance level multimodular fusion regarding text, audio, and video features is developed. Finally, the proposed multikernal extreme learning classifier (MKELM) is employed for sentiment classification. Then the proposed system is evaluated by testing with three multimodal datasets in terms of precision, accuracy, recall, and F-measure, respectively. From the simulation, it is clear that the proposed system accomplishes the maximum classification accuracy value of 98.3% with minimum computation time. The implementation of our proposed approach is done under the MATLAB platform.

中文翻译：

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混合草蜂优化-多核极限学习分类器：社交媒体视频中多模态情感分析的多模融合策略和最优特征选择

目前，像音频和视频这样的大型录音每天都会上传到公共网络，特别是 Facebook、YouTube 等。这样的帖子通过 Internet 产生无限的数据。在接下来的几十年中，适应和处理数据挖掘以从社交媒体获取相关细节被认为是一项艰巨的任务。为了应对这些挑战，本文提出了一种新颖的文本-音频-视频一致性驱动的多模态情感分析方法。该提议的系统检查文本、音频和视频之间的相关性，然后进行多模态情感分析。提取一组不同的特征，然后通过采用新的混合草蜂优化算法 (HGBEE) 对提取的特征进行最佳选择，从而获得包含最佳值的特征集，以实现更高的精度和更低的计算时间。然后开发了关于文本、音频和视频特征的话语级多模融合。最后，提出的多核极限学习分类器（MKELM）用于情感分类。然后通过分别在精度、准确度、召回率和 F 度量方面对三个多模态数据集进行测试来评估所提出的系统。从仿真中可以看出，所提出的系统以最少的计算时间实现了 98.3% 的最大分类准确率值。我们提出的方法的实现是在 MATLAB 平台下完成的。然后通过分别在精度、准确度、召回率和 F 度量方面对三个多模态数据集进行测试来评估所提出的系统。从仿真中可以看出，所提出的系统以最少的计算时间实现了 98.3% 的最大分类准确率值。我们提出的方法的实现是在 MATLAB 平台下完成的。然后通过分别在精度、准确度、召回率和 F 度量方面对三个多模态数据集进行测试来评估所提出的系统。从仿真中可以看出，所提出的系统以最少的计算时间实现了 98.3% 的最大分类准确率值。我们提出的方法的实现是在 MATLAB 平台下完成的。