Sentiment Analysis in Education

Introduction

Teacher effectiveness can be gauged by analysis of student sentiment as communicated in written texts, particularly in social media posts (Misuraca, Forciniti, Scepi & Spano, 2020). Hajirizi and Nuci (2020) state that being able to understand and find out what students like and dont like most about a course, professor, or teaching methodology can be of great importance for the respective institutions (p. 1). For Kwecko et al. (2020), the challenge is to understand the points of convergence and divergence between a set of opinions published on digital networks and their ability to reveal collective intelligences for the management of public policies in Education (p. 2).

To overcome that challenge, both Hajirizi and Nuci (2020) and Nikolic, Grljevic and Kovacevic (2020) recommend aspect-based sentiment analysis (ABSA) to help discern student sentiment. The use of ABSA appears to be a major trend in gauging sentiment analysis in education.

Sentiment Analysis

Misuraca et al. (2020) explain that processing data to engage in sentiment analysis requires significant computational effort (p. 1). As a sub-discipline of natural language processing, ABSA allows analysts to focus their attention on sentiments and their targets as revealed in a students written or posted sentence. Yet as Nikolic et al. (2020) explain, students in higher education often communicate diverse sentiments with multiple targets in one sentence, and ABSA systems are generally not developed to withstand this level of complexity. For instance, if a student were to make the following postThe professor is excellent, but the course materials are poorly organizedit would not be analyzed properly (Nikolic et al., 2020, p. 2). To overcome this defect, Nikolic et al. (2020) developed an ABSA system that could break sentences into clauses and phrases by way of a custom splitter so as to assign clauses and phrases a tag of positive or negative sentiment. This is just one example of how researchers are using technology to help benefit the overhaul of educational approaches. As Misuraca et al. (2020) put it, determining students views by collecting and processing feedback on their learning experiences is widely recognized as a central strategy for assessing the quality of teaching at most educational institutions (p. 2). Through the text mining process, involving data collection and assembly, data processing, data exploration and visualization, model building, and model evaluation, an iterative approach to understanding student sentiment can be used to achieve this aim (Hajirizi & Nuci, 2020).

One obstacle that presents itself to this process, however, is the obstacle of...

…can limit educators perspective on student sentiment (Misuraca et al. (2020). The more that educators are able to apply advances in sentiment analysis, using techniques such as ABSA, the more likely they are to be able to augment their own approaches to education to help both themselves and their students achieve their goals.

Conclusion

Sentiment analysis helps researchers to understand what people are thinking and feeling by analyzing texts that are published, most notably online via social media. In education, sentiment analysis is playing a key role in helping educators to understand student sentiment regarding courses and course material. Up till now, educators have relied mainly on surveys that they give to students to obtain feedback. However, surveys limit ones insight because they do not generally give the student an opportunity or occasion to voice matters in his own words. Social media platforms do provide that opportunity, and sentiment analysis can be used to read the text and determine whether students have a positive, negative or neutral outlook on a particular course or approach to education. Educators can use this information to adjust or reinforce their own pedagogy, and students can benefit from sentiment analysis because it means their voices are being heard and used to augment their education. The educators…