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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (6): 103-114    DOI: 10.11925/infotech.2096-3467.2020.1159
Current Issue | Archive | Adv Search |
Sentiment Classification of Image-Text Information with Multi-Layer Semantic Fusion
Xie Hao,Mao Jin(),Li Gang
Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This paper conducts sentiment analysis of images and text on social media data, aiming to better understand the public's emotions and opinion tendencies. [Methods] To fully explore the correlation and complementarity between images and text, this paper proposes an image-text sentiment classification model in social media based on multi-layer semantic fusion. There are three sub-models in our study: text-image semantic association model, image-text semantic association model, and multimodal semantic deep association fusion model. We used these sub-models to explore the bidirectional and multi-level semantic associations between images and text. Then, we obtained the final classification results using a weighting strategy on the sentiment classification scores generated by the three sub-models. [Results] We examined our model with real image-text data sets and found it achieved the best performance in all evaluation metrics. The accuracy and F1 values of our model were 1.0% and 1.2% better than those of the optimal baseline model. [Limitations] We only evaluated the model’s performance with one single dataset. More research is needed to examine the robustness and scalability of the model. [Conclusions] In the sentiment classification task, the proposed model could more effectively explore the correlation and complementarity between image and text information on social media.

Key wordsImage-Text Fusion      Attention Mechanism      Multi-Modality      Sentiment Classification      Social Media     
Received: 24 November 2020      Published: 10 March 2021
ZTFLH:  G350  
Fund:National Natural Science Foundation of China(71790612);National Natural Science Foundation of China(71921002)
Corresponding Authors: Mao Jin     E-mail: danveno@163.com

Cite this article:

Xie Hao,Mao Jin,Li Gang. Sentiment Classification of Image-Text Information with Multi-Layer Semantic Fusion. Data Analysis and Knowledge Discovery, 2021, 5(6): 103-114.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.1159     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I6/103

Examples of Image-Text Data
The Framework of Image-Text Sentiment Classification Based on Multi-layer Semantic Fusion
The Framework of Text-Image Semantic Association Model
The Framework of Image-Text Semantic Association Model
The Framework of Multimodal Semantic Deep Association Fusion Model
模态 参数名称 参数值
文本 GloVe维度 100
文本最大长度
LSTM神经元
50
1 024
图片 图像尺寸 224×224
ResNet输出层 conv4_block6_out
其他 Dropout
优化函数
学习率
批量规模
损失函数
0.5
RMSprop
1×10-5
20
binary_crossentropy
Setting of Important Parameters
方法 算法 Accuracy Recall Precision F1
基准方法 STM 0.768 0.780 0.749 0.764
SIM 0.810 0.772 0.823 0.797
Early Fusion
Late Fusion
DMAF
0.840
0.830
0.856
0.813
0.817
0.801
0.848
0.828
0.889
0.831
0.823
0.843
本文方法 TISAM 0.812 0.763 0.833 0.797
ITSAM 0.832 0.755 0.880 0.813
MSDAFM
MSDAFM-L
0.852
0.866
0.817
0.817
0.868
0.895
0.843
0.855
Algorithm Performance
α and β
">
Accuracy at Different Values of α and β
α and β
">
F1 Value at Different Values of α and β
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