## 5 Results
### Analyzing Importance of Scientific Articles Using Citation Sentiment  
In addition to the sentiment classification, the information provided in dataset-1 can be used to analyze the quality of an article by analyzing the number of citations and the ratio of positive, negative, and neutral sentiments regarding that article. For this purpose, the articles with at least 100 citations are selected from dataset-1, and the distribution of the citations is given in Figure 2. Each sub-figure is drawn for one article, and only articles that are cited by 100 or more papers are included. The Figures show the distribution of the citation sentiments of a specific scientific article within other articles, which means the positive, negative, or neutral context in whichever paper is cited. For example, if the paper is praised for its contribution, the sentiment is positive; if it is criticized, the citation sentiment is negative, and so on. This indicates that neutral citations are predominant for research articles, followed by positive citations, while negative citations are the lowest on average. Currently, authors' ranking is determined solely by the number of citations \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline CNN & 93.47\% & 94.24\% & 96.18\% & 95\% \\ \hline LSTM & 89.24\% & 91.36\% & 93.21\% & 92.28\% \\ \hline RF & 87.87\% & 89\% & 92\% & 90.05\% \\ \hline SGD & 88.13\% & 91\% & 91\% & 91\% \\ \hline LR & 91.24\% & 90\% & 88\% & 89\% \\ \hline VC (LR + SGD) & 92.54\% & 92\% & 93\% & 92.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results using the combined features. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **fastText** & **fastText Subword** & **GloVe** & **Combined Features** \\ \hline CNN & 89.67\% & 90.42\% & 89.81\% & 92.25\% \\ \hline LSTM & 88.88\% & 89.63\% & 91.11\% & 91.89\% \\ \hline RF & 89.25\% & 89.10\% & 87.62\% & 91.65\% \\ \hline SGD & 86.31\% & 86.89\% & 88.65\% & 89.25\% \\ \hline LR & 86.24\% & 87.21\% & 87.23\% & 90.11\% \\ \hline VC (LR + SGD) & 88.45\% & 88.21\% & 89.65\% & 91.73\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results for dataset-2 using all features. their works have recieved, which is not correct, as the majority of the citations do not attest to the scientific significance of the articles. Instead, a higher number of citations is neutral. Considering the positive sentiments would be much more appropriate for analyzing the true potential of an article. Similarly, several articles have a higher number of negative citations, say, for example, Figure 2b,f, where the number of positive and negative citations are almost equal; however, both types of citations are considered as positive when determining the importance of those articles. Similarly, Figure 2l shows a higher number of negative citations, yet, negative citations are counted analogously to those that are positive and neutral. Dataset-1 does not contain the information of the cited article, so the analysis regarding the quality of the article can not be carried out further. The role of sentiment citation can be very influential for determining the true importance of a scientific article, as not every citation is made to acknowledge the superiority of a research article. However, for this purpose, a specialized dataset is needed that contains the citation text, citation sentiment, and the information of the citing and the cited article. For this purpose, we additionally collected a dataset of several articles that have a high number of citations. For this purpose, we selected four articles with citations higher than 1000, as follows:  
1. _\"New Avenues in Opinion Mining and Sentiment Analysis\"_;
2. _\"Recent Trends in Deep Learning Based Natural Language Processing\"_;
3. _\"Techniques and applications for sentiment analysis\"_;
4. _\"Sentiment analysis algorithms and applications: A survey\"_.