# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
## 4 Systematic Mapping Study Results
### Findings Concerning RQs
**_What are the most common sources used to collect students' feedback?_  
Based on the literature review in preparing this study, we came across several data sources, and based on their characteristics, we divided them into the four following categories for the convenience of our readers and the researchers working in this domain. The categories are as follows:  
* Social media, blogs and forums: This category of datasets consists of data collected from online social networking and micro-blogging sites, discussion forums etc., such as Facebook and Twitter;
* Survey/questionnaires: This category comprises data that were mostly collected by conducting surveys among students and teachers or by providing questionnaires to collect feedback from the students;
* Education/research platforms: This category contains the data extracted from online platforms providing different courses such as Coursera, edX, and research websites such as ResearchGate, LinkedIn, etc. ;
* Mixture of datasets: In this category, we grouped all those studies which used several datasets to conduct their experiments. As can bee seen in Figure 10, there were only 64 (69.57%) papers that reported the sources from which the data were collected, whereas almost one-third of the papers failed to show any information regarding the sources of datasets. Table 8 shows papers that reported the sources of the datasets used for conducting experiments along with their corresponding categories and description. **RQ6. **_What are the solutions with respect to the packages, tools, frameworks and libraries utilized for sentiment analysis?_  
Sentiment analysis is still a new field, and therefore there is no single solution/approach that dominates in sentiment analysis systems. In fact, there are dozens of solutions in terms of packages, frameworks, libraries, tools, etc. that are widely used across application  
Figure 10: Categories of sources of the datasets. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Dataset Category** & **Papers** & **Description** \\ \hline Social media, blogs, and forums & [12; 35; 37; 38; 52; 57; 59; 63; 64; 68; 77; 80; 81; 87; 89; 93] & This category of datasets consists of data collected from online social networking and micro-blogging sites, discussion forums etc. such as Facebook and Twitter \\ \hline Survey/questionnaire & [13; 15; 32; 33; 41; 51; 57; 60; 62; 65; 71; 77; 98; 83; 89; 94; 96; 100] & Here, the data were mostly collected by conducting surveys among students and teachers or by providing questionnaires to collect feedback from the students \\ \hline Education/research platforms & [14; 31; 36; 40; 44–46,48; 58; 61; 70; 78; 82; 84; 86; 93; 95; 99; 101] & This category contains the data extracted from online platforms providing different courses such as Coursera, edX, and research websites such as ResearchGate, LinkedIn, etc. \\ \hline Mixture of datasets & [34; 42; 43; 47; 49; 53; 67; 68; 85; 97; 98] & In this category, we grouped all those studies which used several datasets to conduct their experiments \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Sources of datasets used across reviewed papers. domains in general, and the education domain in particular. Figure 11 shows the findings of articles reviewed in this study with respect to the most commonly used packages, tools, libraries, etc. for the sentiment analysis task. As shown in the Treemap illustrated in Figure 11, Python-based NLP and machine learning packages, libraries, and tools (colored in blue) are among the most popular solutions due to the open-source nature of the Python programming language. Specifically, the NLTK (Natural Language Toolkit) package is the dominant solution, and it was used in 12 different articles for pre-processing tasks including tokenizing, part-of-speech, normalization, the cleaning of text, etc. Java-based NLP and machine learning packages, frameworks, libraries, and tools constitute the second group of solutions used for sentiment analysis.