# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
## 2 Sentiment Analysis and Related Work  
### Overview of Sentiment Analysis  
Sentiment analysis is a task that focuses on polarity detection and the recognition of emotion toward an entity, which could be an individual, topic, and/or event. In general, the aim of sentiment analysis is to find users' opinions, identify the sentiments they express, and then classify their polarity into positive, negative, and neutral categories. Sentiment analysis systems use NLP and ML techniques to discover, retrieve, and distill information and opinions from vast amounts of textual information [2]. In general, there are three different levels at which sentiment analysis can be performed: the document level, sentence level, and aspect level. Sentiment analysis at the document level aims to identify the sentiments of users by analyzing the whole document. Sentence-level analysis is more fine-grained as the goal is to identify the polarity of sentences rather than the entire document. Aspect-level sentiment analysis focuses on identifying aspects or attributes expressed in reviews and on classifying the opinions of users towards these aspects. As can be seen from Figure 2, the general architecture of a generic sentiment analysis system includes three steps [3]. Step 1 represents the input of a corpus of documents into the system in various formats. This is followed by the second step, which is document processing. At this step, the entered documents are converted to text and pre-processed by utilizing different linguistic tools, such as tokenization, stemming, PoS (Part of Speech) tagging, and entity and relation extraction. Here, the system may also use a set of lexicons and linguistic resources. The central component of the system architecture is the document analysis module (step 3) that also makes use of linguistic resources to annotate the pre-processed documents with sentiment annotations. Annotations represent the output of the system--i.e., positive, negative, or neutral--presented using a variety of visualization tools. Depending on the sentiment analysis form, annotations may be attached differently. For document-based sentiment analysis, the annotations may be attached to the entire documents; for sentence-based sentiments, the annotations may be attached to individual sentences; whereas for aspect-based sentiment, they are attached to specific topics or entities. Sentiment analysis has been widely applied in different application domains, especially in business and social networks, for various purposes. Some well-known sentiment analysis business applications include product and services reviews [4], financial markets [5], customer relationship management [6], and marketing strategies and research [5], among others. Regarding social networks applications, the most common application of sentiment analysis is to monitor the reputation of a specific brand on Twitter or Facebook [7] and explore the reaction of people given a crisis; e.g., COVID-19 [8]. Another important application domain is in politics [9], where sentiment analysis can be useful for the election campaigns of candidates running for political positions. Recently, sentiment analysis and opinion mining has also attracted a great deal of research attention in the education domain [2]. In contrast to the above-mentioned fields of business or social networks, which focus on a single stakeholder, the research on sentiment analysis in the education domain considers multiple stakeholders of education including teachers/instructors, students/learners, decision makers, and institutions. Specifically, sentiment analysis is mainly applied to improve teaching, management, and evaluation by analyzing learners' attitudes and behavior towards courses, platforms, institutions, and teachers.