# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
## 4 Systematic Mapping Study Results
### Findings Concerning RQs
In addition to this, some studies employed statistical-based metrics to assess the accuracy of systems. \begin{table}
\begin{tabular}{c c} \hline \hline
**Lexicon-Based** & **Papers** \\ \hline VADER & [55; 60; 62; 68; 99] \\ Sentiwordnet & [57; 78; 83; 91] \\ TextBlob & [55; 69] \\ MPQA & [42] \\ Sentistrength & [94] \\ Semantria & [61; 79] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Most frequently used lexicons. \begin{table}
\begin{tabular}{c c} \hline \hline
**Learning Approach** & **Papers** \\ \hline Supervised & [14; 18; 24; 25; 26; 27; 28; 29; 30; 31; 33; 35; 36; 39; 42; 55; 68; 71; 72; 75; 76; 77; 78; 80; 85; 89; 90] \\ Unsupervised & [51; 52; 53] \\ Lexicon-based & [15; 54; 55; 56; 57; 54; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67] \\ Supervised and unsupervised & [68; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 13; 72; 84; 86; 87; 88; 13; 89; 14; 15; 16; 17; 18; 19; 15; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48;It is very interesting to depict the number of articles that used a specific evaluation metric to assess the performance of systems versus the number of articles that either did not perform any evaluation or decided not to emphasize the used metrics. Figure 7 illustrates the evaluation metrics used and emphasizes the percentage of articles defined for a particular metric. As can be seen from Figure 7, 68% of the articles included either only the F1-score or other evaluation metrics including the F1-score, precision, recall, and accuracy. Only 3% of the studies used Kappa, 2% used the Pearson r-value, and the remaining 27% did not specify any evaluation metrics. **RQ4**. _In which bibliographical sources are the metrics published and what are the research trends and patterns?_  
The publication trend during the review period included in this paper indicated that there was a variation regarding the distribution of publications across years and bibliographic resources. According to our findings, as illustrated in Figure 8, it is obvious that the majority of the papers were published during 2019, where Springer and IEEE were the most represented bibliographical sources. It is also interesting to note that during 2017, there were only three resources in which papers on sentiment analysis were published. For a better overview, we present the absolute number of publications across years with the publishers' details in Table 7. This will assist readers to swiftly identify the time period and place of publication of the reviewed articles. Regarding the applied techniques, there were only two major categories of techniques used to conduct sentiment analysis in the education domain during 2015 and 2017: NLP and ML. The first efforts [12; 32] towards applying DL were presented during 2018, as shown in Figure 9. Moreover, an increasing research pattern of DL application appeared in 2019 and 2020--especially during 2020, where an equal distribution of DL versus the other techniques can be observed. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Publisher** & **\#Articles Published** & **Time Period** \\ \hline Elsevier & 6 & 2015–2020 \\ IEEE & 41 & 2015–2020 \\ ACM & 6 & 2016–2020 \\ Springer & 17 & 2015–2020 \\ Wiley & 2 & 2018–2020 \\ Ceur-WS & 2 & 2018–2019 \\ BEIESP, ArXiv & 2 (each publisher) & 2019 \\ ET and ASR, Erudit, Techscience & 1 (each publisher) & 2020 \\ Emerald, IAES, JUCS, Res. Trend, T. and Francis & 1 (each publisher) & 2019 \\ RMI & 1 & 2017 \\ Hindawi, ACL Ant. & 1 (each publisher) & 2016 \\ Ripublication, TUBITAK & 1 (each publisher) & 2015 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Number of articles published between 2015 to 2020 by selected publishers. Figure 7: Evaluation metrics applied in the reviewed papers. Figure 8: Distribution of publications across years and bibliographic sources. Figure 9: Techniques used for sentiment analysis across years. **RQ5.