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首页> 外文会议>International conference on mining intelligence and knowledge exploration >Multimodal Sentiment Analysis Using Deep Neural Networks
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Multimodal Sentiment Analysis Using Deep Neural Networks

机译：使用深度神经网络的多峰情感分析

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摘要

Due to increase of online product reviews posted daily through various modalities such as video, audio and text, sentimental analysis has gained huge attention. Recent developments in web technologies have also enabled the increase of web content in Hindi. In this paper, an approach to detect the sentiment of an online Hindi product reviews based on its multi-modality natures (audio and text) is presented. For each audio input, Mel Frequency Cepstral Coefficients (MFCC) features are extracted. These features are used to develop a sentiment models using Gaussian Mixture Models (GMM) and Deep Neural Network (DNN) classifiers. From results, it is observed that DNN classifier gives better results compare to GMM. Further textual features are extracted from the transcript of the audio input by using Doc2vec vectors. Support Vector Machine (SVM) classifier is used to develop a sentiment model using these textual features. From experimental results it is observed that combining both the audio and text features results in improvement in the performance for detecting the sentiment of an online product reviews.
机译：由于每天通过视频，音频和文本等各种方式发布的在线产品评论的增加，情感分析已引起了广泛的关注。 Web技术的最新发展也使印地语中的Web内容得以增加。在本文中，提出了一种基于在线多语种性质（音频和文本）来检测在线北印度语产品评论情绪的方法。对于每个音频输入，都会提取梅尔频率倒谱系数（MFCC）功能。这些功能用于使用高斯混合模型（GMM）和深度神经网络（DNN）分类器来开发情感模型。从结果可以看出，与GMM相比，DNN分类器给出了更好的结果。使用Doc2vec向量从音频输入的抄本中提取更多文本特征。支持向量机（SVM）分类器用于使用这些文本功能来开发情感模型。从实验结果可以看出，将音频和文本功能结合在一起可以提高检测在线产品评论情绪的性能。

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