Predicting Students’ Emotions Using Machine Learning Techniques

Altrabsheh, Nabeela; Cocea, Mihaela; Fallahkhair, Sanaz

doi:10.1007/978-3-319-19773-9_56

Cited by 25 publications

(8 citation statements)

References 5 publications

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“…Multimonial Naive Bayes has been used in sarcasm detection by Justo et al [3], obtaining a recall of 77%. We notice that interestingly, CNB performs the lowest in terms of accuracy, despite previous research showing that it performs well with uneven classes [1], which is applicable to our dataset as the sarcasm class represents only 13% of the data; however, CNB led to the highest sarcasm recall of 63%.…”

Section: Resultscontrasting

confidence: 53%

“…Naive Bayes has not been used before to detect sarcasm, however, previous research shows that it is performs well in sentiment analysis [1]. Multimonial Naive Bayes has been used in sarcasm detection by Justo et al [3], obtaining a recall of 77%.…”

Section: Resultsmentioning

confidence: 99%

“…amused, bored, confused, frustrated, and to provide a polarity label (positive/ negative/ neutral). A list of all the emotions can be found in [1]. 1522 tweets were collected with their corresponding emotion and polarity labels.…”

Section: Predicting Sarcasm From Students' Feedbackmentioning

confidence: 99%

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Detecting Sarcasm from Students’ Feedback in Twitter

Altrabsheh

Cocea

Fallahkhair

2015

Lecture Notes in Computer Science

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Abstract. Sarcasm is a sophisticated form of act where one says or writes the opposite of what they mean. Sarcasm is a common issue in sentiment analysis and detecting it is a challenge. While models for sarcasm detection have been proposed for general purposes (e.g. Twitter data, Amazon reviews), there is no research addressing this issue in an educational context, despite the increased use of social media in education. In this paper we experiment with several machine learning techniques, features and preprocessing levels to identify sarcasm from students' feedback collected via Twitter.

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Section: Resultscontrasting

confidence: 53%

Section: Resultsmentioning

confidence: 99%

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Detecting Sarcasm from Students’ Feedback in Twitter

Altrabsheh

Cocea

Fallahkhair

2015

Lecture Notes in Computer Science

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“…Due to the popularity of probabilistic approaches in traditional machine learning, SVM and NB have been used for discriminating one emotion from the other ones (Altrabsheh et al 2015). However, as argued in , different emotions are not really mutually exclusive, i.e.…”

Section: Review Of Recognition-intensive Classificationmentioning

confidence: 99%

Fuzzy rule-based systems for recognition-intensive classification in granular computing context

Liu

Zhang

2018

Granul. Comput.

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In traditional machine learning, classification is typically undertaken in the way of discriminative learning using probabilistic approaches, i.e. learning a classifier that discriminates one class from other classes. The above learning strategy is mainly due to the assumption that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in the context of real-life data classification, especially when the nature of a classification task is to recognize patterns of specific classes. For example, in the context of emotion detection, multiple emotions may be identified from the same person at the same time, which indicates in general that different emotions may involve specific relationships rather than mutual exclusion. In this paper, we focus on classification problems that involve pattern recognition. In particular, we position the study in the context of granular computing, and propose the use of fuzzy rule-based systems for recognition-intensive classification of real-life data instances. Furthermore, we report an experimental study conducted using 7 UCI data sets on life sciences, to compare the fuzzy approach with four popular probabilistic approaches in pattern recognition tasks. The experimental results show that the fuzzy approach can not only be used as an alternative one to the probabilistic approaches but also is capable to capture more patterns which probabilistic approaches cannot achieve.

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“…In this case, it is difficult for instructors to identify the struggling, confused and even frustrated students. Previous research has indicated that positive emotion helps to promote students' learning interests and engagement levels (Altrabsheh, Cocea, & Fallahkhair, 2015;D'Mello et al, 2008), and students with an upsurge of emotion may have higher motivation to accomplish their learning goals. Ramesh, Goldwasser, Huang, Daumé III, & Getoor (2013) incorporated the positive/negative score of post content into engagement metrics to distinguish between disengaged and engaged learners in MOOC forums.…”

Section: Emotional States Of Students In Course Forumsmentioning

confidence: 99%

Exploring Students’ Engagement Patterns in SPOC Forums and their Association with Course Performance

Zhi

Pinkwart

Liu

et al. 2018

EURASIA J MATH SCI T

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With the popularity of Small Private Online Courses (SPOCs) in higher education, a plentiful of discussion data has been increasingly generated in SPOC forums. With 752 undergraduates' discussion posts, this study aims to investigate students' engagement patterns within SPOC forums in terms of engagement behaviors and emotions. Firstly, we designed a behavioral code rule to identify posting-and content-level behaviors, and examined their association with course performance. Secondly, we built an emotion lexicon including positivity, negativity and confusion word sets, and adopted an emotion calculation approach to visualize emotional evolutionary trends and to examine emotional differences in registration types and course performance. The results show that, (1) the high-performing group was more active in the most engagement behaviors except for interactive postings. (2) The registered group delivered more threads and wrote richer vocabulary in post content. (3) Whether students were registered for a course or not did not have a significant effect on their emotional expressions, but the registered group exhibited more confusion in forum interactions at the end of the semester. (4) Positive emotion was prevailing for the entire population. Furthermore, compared with the low-achieving group, the highperforming group had higher emotion densities in three types of emotions.

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Predicting Students’ Emotions Using Machine Learning Techniques

Cited by 25 publications

References 5 publications

Detecting Sarcasm from Students’ Feedback in Twitter

Detecting Sarcasm from Students’ Feedback in Twitter

Fuzzy rule-based systems for recognition-intensive classification in granular computing context

Exploring Students’ Engagement Patterns in SPOC Forums and their Association with Course Performance

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