# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
###### Abstract  
In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students' opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students' feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection. sentiment analysis; opinion mining; student feedback; user reviews; teacher assessment; educational platforms; MOOCs; natural language processing; text mining; deep learning; machine learning; polarity assessment; emotion recognition +
Footnote †: journal: 28 April 2021
are one's feelings expressed regarding a given topic. Since the 1960s, several theories about emotion detection and classification have been developed. The study conducted by [PERSON] [1] categorizes emotions into eight categories: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. Sentiment analysis can be conducted at a word, sentence, or a document level. However, due to the large number of documents, manual handling of sentiments is impractical. Therefore, automatic data processing is needed. Sentiment analysis from the text-based, sentence or document-level corpora is employed using natural language processing (NLP). Most research papers found in the literature published until 2016-2017 employed pure NLP techniques, including lexicon and dictionary-based approaches for sentiment analysis. Few of those papers used conventional machine learning classifiers. Recent years have seen a shift from pure NLP-based approaches to deep learning-based modeling in recognizing and classifying sentiment, and the number of papers published recently on the undertaken topic has increased significantly. The popularity and importance of students' feedback have also increased recently, especially in the times of the COVID-19 pandemic, when most educational institutions have transcended traditional face-to-face learning to the online mode. Figure 1 shows the country-wise comparison breakdown of interest over the past six years in the use of sentiment analysis for analyzing students' attitudes towards teacher assessment. The number of papers published recently indicates a growing interest towards the application of NLP/DL/ML solutions for sentiment analysis in the education domain. However, to the best of our knowledge, in order to establish the state of evidence, the body of literature is lacking a review that systematically classifies and categorizes research and results by showing the frequencies and visual summaries of publications, trends, etc.