# Visual Enhancement Capsule Network for Aspect-Based Multimodal Sentiment Analysis
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.3 Aspect-Based Multimodal Sentiment Analysis  
We conduct the experiment on the multimodal datasets of Multi-ZOL and MTCom, and compare our model with the baseline MIMN model. The results of the two models are listed in the last two rows in Table 5. It can be observed that the experimental results of VECapsNet proposed in our method on the Multi-ZOL dataset are close to and a bit lower than MIMN, but the result on the MTCom dataset is significantly better than MIMN. Since the same model behaves differently on two datasets, we will analyze the experimental results from two aspects of the composition of the data set and the limitations of the model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Lap14**} & \multicolumn{2}{c}{**Rest14**} & \multicolumn{2}{c}{**Rest15**} & \multicolumn{2}{c}{**Rest16**} \\ \cline{2-7}  & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** \\ \hline ATAE-LST & 68.70 & - & 77.20 & - & - & - & - \\ MemNet & 70.64 & 65.17 & 79.61 & 69.64 & 77.31 & 58.28 & 85.44 & 69.55 \\ IACapsNet & 76.80 & 73.29 & 81.79 & 73.40 & 80.41 & 61.67 & 88.71 & 64.48 \\ MemCapsNet (our) & **77.56** & **74.14** & **83.56** & **74.78** & **80.41** & **61.67** & **89.16** & **71.62** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The metrics of accuracy and F1-score on four text datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Multi-ZOL**} & \multicolumn{2}{c}{**MTCom**} \\ \cline{2-5}  & **Accuracy** & **F1** & **Accuracy** & **F1** \\ \hline MemNet-T & 59.51 & 58.73 & 56.08 & 53.03 \\ IACapsNet-T & 58.69 & 57.47 & 53.48 & 50.77 \\ MemCapsNet (our) & **59.47** & **58.91** & **56.32** & **54.71** \\ \hline VGG-16-1 & 44.05 & 43.67 & 33.46 & 31.86 \\ CapsNet-1 & 45.10 & 44.29 & 31.41 & 30.13 \\ AgCapsNet (our) & **46.59** & **44.36** & **41.37** & **40.16** \\ \hline MIMN & **61.59** & **60.51** & 54.35 & 52.94 \\ VECapsNet (our) & 61.23 & 59.63 & **57.87** & **56.81** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The metrics of accuracy and F1-score on the multimodal datasets. Analysis of datasets.Multi-ZOL is a multimodal sentiment annotated dataset provided by [PERSON] et al. [10] for evaluating the MIMN model, which contains six aspects: \"price-performance ratio\", \"performance configuration\", \"battery life\", \"appearance and feeling\", \"photographing effect\", and \"screen\". Except \"price-performance ratio\", the corresponding affective entities with the aspects can be intuitively found in the images in reviews. For example, \"performance configuration\" corresponds to the run points' scores with different colors or the line charts and other statistical charts; \"battery life\" corresponds to the battery consumption diagrams; \"appearance and feeling\" corresponds to the photos of mobile phones; \"photographing effect\" corresponds to the close-range or long-range photos; \"screen\" corresponds to the lighted mobile phone screens, etc. In the MTCom dataset, for the annotated aspects of \"general comment\", \"location\", \"environment\", \"prices\", \"tastes\", and \"service\", it is difficult to find their corresponding affective entities in the review images, except for \"environment\" and \"tastes\". Therefore, the reason for the performance differences of the model in the two datasets is likely to be the non-correspondence between the aspects and the affective entities of images in most of reviews. At the same time, we also further explore the compositions of the text content and images in two datasets: Multi-ZOL and MTCom.