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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.12
- 수록면
- 125 - 137 (13page)
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Social network has become an integral part of our daily life. Sentiment analysis of social media information is helpful to understand people's views, attitudes and emotions on social networking sites.
Traditional sentiment analysis mainly relies on text. With the rise of smart phones, information on the network is gradually diversified, including not only text, but also images. It is found that, in many cases, images can enhance the text rather than express emotions independently. We propose a novel image text sentiment analysis model (LSTM-VAA). Specifically, this model does not take the picture information as the direct input, but uses the VGG16 network to extract the image features, and then generates the visual aspect attention, and gives the core sentences in the document a higher weight, and get a document representation based on the visual aspect attention. In addition, we use the LSTM network to extract the text sentiment and get the document representation based on text only. Finally, we fuse the two groups of classification results to obtain the final classification label.
On the yelp restaurant reviews data set, our model achieves an accuracy of 62.08%, which is 18.92% higher than BiGRU-m VGG, which verifies the effectiveness of using visual information as aspect attention assisted text for emotion classification; It is 0.32% higher than Vista-Net model, which proves that LSTM model can effectively make up for the defect that images in Vista-Net model cannot completely cover text.
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참고문헌 (28)
참고문헌 신청
LI X / 2014 / News impact on stock price return via sentiment analysis / Proceedings of the Knowledge Based System 69:14~23
KAGAN V / 2014 / Using twitter sentiment to forecast the 2013 Pakistani election and the 2014 Indian election / Proceeding of the IEEE Intelligent Systems 30(1):2~5
YADAV S / 2018 / Medical sentiment analysis using social media:towards building a patient assisted system / Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018). :2790~2797
TRUONG T Q / 2019 / VistaNet: Visual Aspect Attention Network for Multimodal Sentiment analysis / Proceedings of The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-2019). :305~312
YU D / 2017 / Multilevel attention networks for visual question answering / Proceedings of the Computer Vision and Pattern Recognition (CVPR). :4187~4195
KAGAN V / 2014 / Using twitter sentiment to forecast the 2013 Pakistani election and the 2014 Indian election / Proceeding of the IEEE Intelligent Systems 30(1):2~5
YADAV S / 2018 / Medical sentiment analysis using social media:towards building a patient assisted system / Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018). :2790~2797
TRUONG T Q / 2019 / VistaNet: Visual Aspect Attention Network for Multimodal Sentiment analysis / Proceedings of The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-2019). :305~312
YU D / 2017 / Multilevel attention networks for visual question answering / Proceedings of the Computer Vision and Pattern Recognition (CVPR). :4187~4195
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