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

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

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

Investigation of low cost techniques for realising microwave and millimeter-wave network analysers.

The work presented in this thesis is on the development of reliable low cost measurement systems for measuring microwave and millimetre-wave devices. The purpose of this work is to find techniques which use multiple power detectors and can measure magnitude and phase without the need for expensive superheterodyne receivers. Two novel microwave measurement systems have been designed with the intention of providing a measurement facility which enables the characterisation of both active and passive devices in terms of their scattering parameters. The first method is based on using a multistate reflectometer, which uses dielectric waveguide in the frequency range of 110GHz up to 170GHz. The dielectric multistate reflectometer is a four-port reflectometer, which uses a programmable phase shifter to give a flat relative phase shift over the entire frequency range of the dielectric waveguides used in the multistate reflectometer. The phase shifter has an eccentric rotating cylinder with an offset axis to allow a number of different phase shifts to the wave travelling in the dielectric waveguides in the multistate reflectometer. This system has been developed as an equivalent to a one-port network analyser. The second method is based on using the multi-probe reflectometer in which the standing wave in a line is measured using a number of fixed detector probes. A microstrip line prototype in the frequency range of 1GHz to 5.5GHz has been demonstrated and the design of a monolithic microwave integrated circuit (MMIC) version for the frequency range of 40GHz to 325GHz has been earned out. Improved methods of calibration of the system have been derived as well as different methods for error correction. The realisation of a full two-port network analyser using the technique has been demonstrated. Key words: dielectric multistate reflectometer, programmable phase shifter, multi-probe reflectometer, detection, microwave measurement, millimetre-wave measurement, calibration, error corrections

Investigation of low cast techniques for realising microwave and millimeter-wave network analysers

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Investigation of low cost techniques for realising microwave and millimeter-wave network analysers.

The work presented in this thesis is on the development of reliable low cost measurement systems for measuring microwave and millimetre-wave devices. The purpose of this work is to find techniques which use multiple power detectors and can measure magnitude and phase without the need for expensive superheterodyne receivers. Two novel microwave measurement systems have been designed with the intention of providing a measurement facility which enables the characterisation of both active and passive devices in terms of their scattering parameters. The first method is based on using a multistate reflectometer, which uses dielectric waveguide in the frequency range of 110GHz up to 170GHz. The dielectric multistate reflectometer is a four-port reflectometer, which uses a programmable phase shifter to give a flat relative phase shift over the entire frequency range of the dielectric waveguides used in the multistate reflectometer. The phase shifter has an eccentric rotating cylinder with an offset axis to allow a number of different phase shifts to the wave travelling in the dielectric waveguides in the multistate reflectometer. This system has been developed as an equivalent to a one-port network analyser. The second method is based on using the multi-probe reflectometer in which the standing wave in a line is measured using a number of fixed detector probes. A microstrip line prototype in the frequency range of 1GHz to 5.5GHz has been demonstrated and the design of a monolithic microwave integrated circuit (MMIC) version for the frequency range of 40GHz to 325GHz has been earned out. Improved methods of calibration of the system have been derived as well as different methods for error correction. The realisation of a full two-port network analyser using the technique has been demonstrated. Key words: dielectric multistate reflectometer, programmable phase shifter, multi-probe reflectometer, detection, microwave measurement, millimetre-wave measurement, calibration, error corrections