.png){kind=icon}
.png){kind=icon}
Return
Predicting Microblog Sentiments via Weakly Supervised Multimodal Deep Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2017.2757769.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Predicting sentiments of multimodal microblogs composed of text, image, and emoticon have attracted ever-increasing research focus recently. The key challenge lies in the difficulty of collecting a sufficient amount of training labels to train a discriminative model for multimodal prediction. One potential solution is to exploit the labels collected from social media users, which is, however, restricted by the negative effect of label noise. Besides, we have quantitatively found that sentiments in different modalities may be independent, which disables the usage of previous multimodal sentiment analysis schemes in our problem. In this paper, we introduce a weakly supervised multimodal deep learning (WS-MDL) scheme toward robust and scalable sentiment prediction. WS-MDL learns convolutional neural networks iteratively and selectively from weak emoticon labels, which are cheaply available and noise containing In particular, to filter out the label noise and to capture the modality dependency, a probabilistic graphical model is introduced to simultaneously learn discriminative multi modal descriptors and infer the confidence of label noise. Extensive evaluations are conducted in a million scale, real-world microblog sentiment dataset crawled from Sina Weibo. We have validated the merits of the proposed scheme by quantitatively showing its superior performance over several stateof-the-art and alternative approaches.
Keywords:
Sentiment prediction
weakly supervised learning
multi-modality
deep learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.7
Papers:
4.5K
Citations:
2.4W