论文标题
在视频评论中挖掘精细颗粒意见的多模式方法
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
论文作者
论文摘要
尽管最近在挖掘书面评论方面取得了进展的进展，但在其他审查来源上，很少有作品解决了问题。鉴于这个问题，我们提出了一种多模式的方法，用于从视频评论中挖掘细粒度的观点，该方法能够确定正在讨论的项目的各个方面以及对它们的情感方向。我们的方法在句子级别上起作用，而无需时间注释，并使用其内容的音频，视频和语言抄录得出的功能。我们在两个数据集上评估了我们的方法，并表明利用视频和音频方式始终提供了比仅文本基线的性能提高的性能，从而提供了证据，这些额外方式是更好地理解视频评论的关键。
Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them. Our approach works at the sentence level without the need for time annotations and uses features derived from the audio, video and language transcriptions of its contents. We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities are key in better understanding video reviews.
