基于机器学习的多模态仇恨言论检测
Multi-modal Hate Speech Detection using Machine Learning
AI总结：
针对单模态仇恨言论检测准确率不足的问题，本研究提出融合图像、音频、文本特征的多模态组合方法，结合机器学习与自然语言处理技术实现视频内容的仇恨言论检测。
摘要：
随着互联网用户与媒体内容的持续增长，追踪音频和视频中的仇恨言论难度极大。将视频或音频转换为文本无法准确检测仇恨言论，因为人类有时会将仇恨词汇用作幽默或善意表达，还会使用不同语调或在视频中呈现不同动作。当前最先进的仇恨言论检测模型大多基于单模态开发。本研究提出了一种多模态系统组合方法，通过提取特征图像、从音频与文本中提取特征值，并运用机器学习和自然语言处理技术，从视频内容中检测仇恨言论。
英文摘要：
With the continuous growth of internet users and media content, it is very hard to track down hateful speech in audio and video. Converting video or audio into text does not detect hate speech accurately as human sometimes uses hateful words as humorous or pleasant in sense and also uses different voice tones or show different action in the video. The state-ofthe-art hate speech detection models were mostly developed on a single modality. In this research, a combined approach of multimodal system has been proposed to detect hate speech from video contents by extracting feature images, feature values extracted from the audio, text and used machine learning and Natural language processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
