# Visual Sentiment Analysis Using Deep Learning Models with Social Media Data
## 2 Literature Survey
When obtaining a high number of labeled data samples is challenging, the efficacy of the learning algorithm suffers. When there is not enough data, the developed deep learning network structure is susceptible to overfitting [39]. Transfer learning, which involves applying knowledge or patterns learned in one field or activity to distinct but related disciplines or challenges, can effectively tackle the problem of limited training data in the appropriate field [40]. As a result, we used the transfer learning strategy for image sentiment polarity with much fewer training examples (1000 image samples) to prevent overfitting and to optimize system performance. To initialize the hyperparameters of our network, we used transfer learning and weights and biases from a pre-trained ImageNet model. With the help of extra layers, such as drop-out and regularization layers (L1 and L2 regularization), we were able to reduce overfitting in our network and increase the system performance. Our system outperformed other systems in visual sentiment analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Author Name** & **Technique Used and Results** & **Merits** & **Limitations** \\ \hline \multirow{3}{*}{1} & [PERSON] et al. [31] & Adjective-noun pairs (ANP) and convolutional neural networks (CNN) trained based on Caffe. The annotated accuracy as well as & The suggested model enhances & To reduce overfitting, network structure must be adjusted. \\  & [31] & proposed DeepSentibank outperformed SentiBank & ANP retrieval performance. & \\  & & 1.1 by 62.3\%. & & \\ \hline \multirow{3}{*}{2} & \(V\)[PERSON] & Fine-tuned CaffeNet CNN architecture was & The model uses fewer training & To accommodate the presence of \\  & et al. [32] & employed. Obtained an accuracy of 0.83 on Twitter & parameters and provides a robust & noisy labels, the architecture must be \\  & & dataset for sentiment prediction. & model for sentiment visualization. & rebuilt. \\ \hline \multirow{3}{*}{3} & [PERSON] et al. [33] & Low-level visual features based on psychology were applied. Accuracy rates of more than 70\% were obtained for classifying a variety of emotions. & Able to generate an emotional & \\  & et al. [33] & & distribution of emotions across & To improve the outcomes, more and \\ \hline \multirow{3}{*}{4} & [PERSON] & RGB histograms, GIST descriptors, and mid-level & Multi-view (text + visual) & Investigation is needed regarding \\  & et al. [34] & features were employed as visual descriptors. On the & embedding space is effective in & features to improve the system \\  & et al. [34] & Flickr dataset, the proposed model achieved an accuracy of 74.77\%. & classifying’visual sentiment. & performance. \\ \hline \multirow{3}{*}{5} & [PERSON] et al. [35] & Unsupervised sentiment analysis framework (USEA) & The ”semanticgap” between low- & More social media sources, such as \\  & [35] & was proposed. Achieved an accuracy score of 59.94\% & and high-level visual aspects was & More social media sources, such as \\ \cline{1-1}  & & for visual sentiment prediction on Flickr dataset. & successfully resolved. & be examined in order to boost \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing works on visual sentiment prediction based on low, mid, and deep visual features.