Sentiment analysis:towards a tool for analysing real-time students feedback

Students' real-time feedback has numerous advantages in education, however, analysing feedback while teaching is both stressful and time consuming. To address this problem, we propose to analyse feedback automatically using sentiment analysis. Sentiment analysis is domain dependent and although it has been applied to the educational domain before, it has not been previously used for real-time feedback. To find the best model for automatic analysis we look at four aspects: preprocessing, features, machine learning techniques and the use of the neutral class. We found that the highest result for the four aspects is Support Vector Machines (SVM) with the highest level of preprocessing, unigrams and no neutral class, which gave a 95 percent accuracy

Learning sentiment from students’ feedback for real-time interventions in classrooms

Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded

Combining Sentiment Lexicons of Arabic Terms

Lexicons are dictionaries of sentiment words and their matching polarity. Some comprise words that are numerically scored based on the degree of positivity/negativity of the underlying sentiments. The ranges of scores differ since each lexicon has its own scoring process. Others use labelled words instead of scores with polarity tags (i.e., positive/negative/neutral). Lexicons are important in text mining and sentiment analysis which compels researchers to develop and publish them. Larger lexicons better train sentiment models thereby classifying sentiments in text more accurately. Hence, it is useful to combine the various available lexicons. Nevertheless, there exist many duplicates, overlaps and contradictions between these lexicons. In this paper, we define a method to combine different lexicons. We used the method to normalize and unify lexicon items and merge duplicated lexicon items from twelve lexicons for (in)formal Arabic. This resulted in a coherent Arabic sentiment lexicon with the largest number of terms

Evaluation of the SA-E system for analysis of students' real-time feedback

Students' real-time feedback is acknowledged as an important source of information for teachers/lecturers to improve their teaching and address issues students may have, such as going deeper in some of the materials covered or providing more examples to understand an abstract concept. Previous applications collecting real-time feedback from students through clickers and mobiles typically collect limited information with pre-defined questions, while more recent applications using social media collect such a large volume of information that a lecturer cannot manually process it in real time. We developed the SA-E system for analysing students' real-time feedback provided via social media, and, in this paper, we present the evaluation of this system in real settings with lecturers and students. The results show that lecturers are highly satisfied with the proposed system. In contrast, although the participation of students in providing feedback was high, the students' opinions of the system were between neutral and dislike.Scopu

Learning sentiment from students' feedback for real-time interventions in classrooms

Abstract. Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded

Detecting sarcasm from students' feedback in Twitter

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

Sentiment analysis: towards a tool for analysing real-time students feedback

Abstract-Students' real-time feedback has numerous advantages in education, however, analysing feedback while teaching is both stressful and time consuming. To address this problem, we propose to analyse feedback automatically using sentiment analysis. Sentiment analysis is domain dependent and although it has been applied to the educational domain before, it has not been previously used for real-time feedback. To find the best model for automatic analysis we look at four aspects: preprocessing, features, machine learning techniques and the use of the neutral class. We found that the highest result for the four aspects is Support Vector Machines (SVM) with the highest level of preprocessing, unigrams and no neutral class, which gave a 95 percent accuracy

Successes and challenges of Arabic sentiment analysis research: a literature review

The analysis of sentiment in text has mainly been focused on the English language. The complexity of the Arabic language and its linguistic features that oppose those found in English resulted in the inability to adapt extant research to Arabic contexts limiting advancement in Arabic sentiment analysis. The need for Arabic sentiment analysis research is accentuated by the driving changes in different Arab regions like heavy political movements in some areas and fast growth in others. These changes help shape not just policies and implications of this region but affect the entire world on a global scale. Therefore, it is essential to utilise effective methods of sentiment analysis to analyse Arabic tweets to understand regional and global implications in microblogging mediums such as Twitter. In this paper, we conduct a comprehensive review of Arabic sentiment analysis, present the pros and cons of the different approaches used and highlight the challenges of it. Finally, we outline the relevant gaps in the literature and suggest recommendations for future Arabic sentiment analysis research. - 2017, Springer-Verlag GmbH Austria.This publication was made possible by the NPRP award [NPRP 7-1334-6-039 PR3] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the author[s].Scopu

Predicting learning-related emotions from students' textual classroom feedback via Twitter.

Teachers/lecturers typically adapt their teaching to respond to students' emotions, e.g. provide more examples when they think the students are confused. While getting a feel of the students' emotions is easier in small settings, it is much more difficult in larger groups. In these larger settings textual feedback from students could provide information about learning-related emotions that students experience. Prediction of emotions from text, however, is known to be a difficult problem due to language ambiguity. While prediction of general emotions from text has been reported in the literature, very little attention has been given to prediction of learning-related emotions. In this paper we report several experiments for predicting emotions related to learning using machine learning techniques and n-grams as features, and discuss their performance. The results indicate that some emotions can be distinguished more easily then others