# Visual Sentiment Analysis Using Deep Learning Models with Social Media Data
## 7 Comparison of Our Work to Existing Image Sentiment Analysis Research
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Author Name** & **Technique Used and Results** & **Merits** & **Limitations** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{[PERSON] et al. [44]} & Convolutional neural networks (CNN) & Using several fusion algorithms, good performance on image sentiment categorization was & A shallow structure may not be adequate for learning high-level semantic \\  & & accuracy of 68.02\%. & & attained. \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{[PERSON] and [PERSON] [45]} & Convolutional neural networks (CNN) & The introduction of deep visual characteristics improved sentiment recognition on social media images. & The performance of using deeper visual features is not consistent across the sentiment categories. \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{[PERSON] and [PERSON] [46]} & A CNN with domain-specific tuning was used: Sentiment prediction on social media data yielded an accuracy of 53.5\%. & \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{[PERSON] and [PERSON]. [47]} & CNN was used in conjunction with Bag-of-Visual-Words (BOVW) features. On the Twitter images dataset, researchers achieved an accuracy of 72.2\% for sentiment prediction. & The performance of sentiment prediction is improved by combining hand-crafted features with CNN features. & To determine the model’s efficiency, a substantial training dataset must be used. \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{[PERSON] and [PERSON] [48]} & To learn the visual features, the Alexnet model was employed. Sentiment prediction from social media images achieved an accuracy score of 48.08\%. & By incorporating label information into the collective matrix factorization (CMF) technique, prediction accuracy is improved. & To achieve better outcomes, more constraints must be applied to the CMF technique, and the Alexnet model must be fine-tuned. \\ \hline \multirow{3}{*}{6} & \multirow{3}{*}{[PERSON] et al. [49]} & SVM classification layer was used on deep CNN architecture. On various visual datasets, the accuracies were 65.89\% and 68.67\%. & The application of attention models ai mapping the local regions of an image, resulting in better sentiment prediction. & To improve sentiment prediction, a strong visual classifier with robust feature identification methodologies is required. \\ \hline \multirow{3}{*}{7} & \multirow{3}{*}{[PERSON] et al. [50]} & The cross-domain semi-supervised deep metric learning (CDSS-DML) method was used. For social media & The model is trained with unlabeled data based on the teacher-student paradigm, & It is necessary to investigate the concept of fine-tuning the model in order to improve its effectiveness. \\ \cline{3-3}  & & image sentiment prediction, it obtained an overall accuracy score of 0.44. & \\ \hline \multirow{3}{*}{8} & \multirow{3}{*}{[PERSON] et al. [51]} & The multisource sentiment generative adversarial network (MSGAN) method was used and, for visual sentiment prediction, an accuracy of 07.63\% was obtained. & Very efficient at dealing with data from numerous source domains. & Methods for improving the inherent flaws of the GAN network must be investigated further. \\ \hline \multirow{3}{*}{9} & \multirow{3}{*}{[PERSON] et al. [52]} & Weakly supervised coupled convolutional network (WSCN) was used. On several datasets of images, the highest accuracy of 0.86\% was obtained for sentiment prediction. & Reduces annotation burden by picking useful soft proposals from weak annotations automatically. & To improve the findings, pre-processing strategies must be investigated. \\ \hline \multirow{3}{*}{10} & \multirow{3}{*}{The proposed approach (fine-tuned pre-trained models), [PERSON] et al. (2018)} & Various fine-tuned pre-trained models, many the VGG-19. ResNet50V2, and DenseNet-211 & By using dropout and regularization layers with fine-tuning, it addresses the training set, and an extra modality (text or audio) can be used. \\ \cline{3-3}  & & & \\ \cline{3-3}  & & & \\ \cline{3-3}  & & & \\ \cline{3-3}  & & & \\ \hline \end{tabular}
\end{table}