Movie Reviews Sentiment Analysis Using BERT

Sentiment analysis (SA) or opinion mining is analysis of emotions and opinions from texts. It is one of the active research areas in Natural Language Processing (NLP). Various approaches have been deployed in the literature to address the problem. These techniques devise complex and sophisticated frameworks in order to attain optimal accuracy with their focus on polarity classification or binary classification. In this paper, we aim to fine-tune BERT in a simple but robust approach for movie reviews sentiment analysis to provide better accuracy than state-of-the-art (SOTA) methods. We start by conducting sentiment classification for every review, followed by computing overall sentiment polarity for all the reviews. Both polarity classification and fine-grained classification or multi-scale sentiment distribution are implemented and tested on benchmark datasets in our work. To optimally adapt BERT for sentiment classification, we concatenate it with a Bidirectional LSTM (BiLSTM) layer. We also implemented and evaluated some accuracy improvement techniques including Synthetic Minority Over-sampling TEchnique (SMOTE) and NLP Augmenter (NLPAUG) to improve the model for prediction of multi-scale sentiment distribution. We found that including NLPAUG improved accuracy, however SMOTE did not work well. Lastly, a heuristic algorithm is applied to compute overall polarity of predicted reviews from the model output vector. We call our model BERT+BiLSTM-SA, where SA stands for Sentiment Analysis. Our best-performing approach comprises BERT and BiLSTM on binary, three-class, and four-class sentiment classifications, and SMOTE augmentation, in addition to BERT and BiLSTM, on five-class sentiment classification. Our approach performs at par with SOTA techniques on both classifications. For example, on binary classification, we obtain 97.67% accuracy, while the best performing SOTA model, NB-weighted-BON+dvcosine,has 97.40% accuracy on the popular IMDb dataset. The baseline, Entailment as Few-Shot Learners (EFL), is outperformed on this task by 1.30%. On the other hand, for five-class classification on SST-5, the best SOTA model, RoBERTa+large+Self-explaining, has 55.5% accuracy, while we obtain 59.48% accuracy. We outperform the baseline on this task, BERT-large, by 3.6%

EXPERIENCES OF BLACK WOMEN STUDENTS IN SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS AT A HISTORICALLY WHITE INSTITUTION IN THE UNITED STATES OF AMERICA: A MULTIPLE CASE STUDY

The purpose of the study was to understand the experiences of Black women students in science, technology, engineering, and mathematics (STEM) at a historically White institution (HWI) with the goal of making recommendations for changes at various levels of the institution. The study sought to answer two overarching research questions: (1) What are the experiences of Black women students in STEM departments at HWIs? (2) In what ways might HWIs and STEM departments influence those experiences? The study employed multiple case study methodology with intersectionality as a multilevel analytical tool to understand Black women students’ experiences. The study identified beliefs, policies, and practices that complicated Black women’s persistence in STEM majors. A major complication was the inequitable engagement of Black women students in co-curricular and extracurricular STEM initiatives for educational enrichment. These findings, which have implications for policy, practice, and future research, are related to the fact that the demographic composition of most STEM departments at this institution could be described as “too White and too male.

Movie Reviews Sentiment Analysis Using BERT

Sentiment analysis (SA) or opinion mining is analysis of emotions and opinions from texts. It is one of the active research areas in Natural Language Processing (NLP). Various approaches have been deployed in the literature to address the problem. These techniques devise complex and sophisticated frameworks in order to attain optimal accuracy with their focus on polarity classification or binary classification. In this paper, we aim to fine-tune BERT in a simple but robust approach for movie reviews sentiment analysis to provide better accuracy than state-of-the-art (SOTA) methods. We start by conducting sentiment classification for every review, followed by computing overall sentiment polarity for all the reviews. Both polarity classification and fine-grained classification or multi-scale sentiment distribution are implemented and tested on benchmark datasets in our work. To optimally adapt BERT for sentiment classification, we concatenate it with a Bidirectional LSTM (BiLSTM) layer. We also implemented and evaluated some accuracy improvement techniques including Synthetic Minority Over-sampling TEchnique (SMOTE) and NLP Augmenter (NLPAUG) to improve the model for prediction of multi-scale sentiment distribution. We found that including NLPAUG improved accuracy, however SMOTE did not work well. Lastly, a heuristic algorithm is applied to compute overall polarity of predicted reviews from the model output vector. We call our model BERT+BiLSTM-SA, where SA stands for Sentiment Analysis. Our best-performing approach comprises BERT and BiLSTM on binary, three-class, and four-class sentiment classifications, and SMOTE augmentation, in addition to BERT and BiLSTM, on five-class sentiment classification. Our approach performs at par with SOTA techniques on both classifications. For example, on binary classification, we obtain 97.67% accuracy, while the best performing SOTA model, NB-weighted-BON+dvcosine,has 97.40% accuracy on the popular IMDb dataset. The baseline, Entailment as Few-Shot Learners (EFL), is outperformed on this task by 1.30%. On the other hand, for five-class classification on SST-5, the best SOTA model, RoBERTa+large+Self-explaining, has 55.5% accuracy, while we obtain 59.48% accuracy. We outperform the baseline on this task, BERT-large, by 3.6%

The High Court of Malawi as a constitutional court: constitutional adjudication the Malawian way

Constitutional adjudication in Malawi only became commonplace after the adoption of a new Constitution in 1994. Like many Anglophone countries, Malawi follows the decentralised model of constitutional adjudication. Under this arrangement, the High Court has unlimited original jurisdiction to hear any civil or criminal matters, including constitutional matters. The Courts Act, however, requires the High Court to sit with an enhanced quorum when it is seized of cases that substantively relate to, or concern the interpretation and application of the Constitution. It is when the High Court sits with a reconfigured quorum that it is popularly referred to as the “constitutional court” (the Court). This article analyses constitutional adjudication in Malawi by focusing on the operation of the Court. Specifically, it analyses the scope of the Court’s jurisdiction, the type of constitutional review that it conducts, the regulation of access to the Court, the forms of decisions and remedies that it grants, and the Court’s independence

Academic freedom, institutional autonomy and the University of Malawi: An analysis of some trends and prospects

No Abstrac

Metacognition Strategies in Solving Mathematics at a Secondary School in Zambia

The main purpose of this study was to explore the metacognition strategies used by learners in solving mathematics problems at a Government Secondary School in Western Province of Zambia. This study was a qualitative case study. The semi-structured interviews were conducted to explore metacognitive strategies pupils used inside and outside the classroom in terms of their teaching experience, active participation, problem solving contexts, corrective feedback utterances and thinking enrichment opportunities during teaching and learning. The unstructured interviews were used to follow up interesting reactions, responses and stories during the mathematics lessons observed.A thematic Analysis technique was conducted where codes, categories and themes were used in analyzing the qualitative data. The codes came out from the actual words of the participants during interviews and observed lessons.  Themes and categories came from the literature reviewed on metacognition. The study found that metacognitive strategies used by the learners were neglected. The study revealed that the main reason for neglecting them was that learners were not aware of them. The findings also indicated that learners were rarely engaged in constructive use of metacognitive strategies in their learning and study of mathematics. The highest used metacognitive strategies were clarifying learner’s ideas, cooperative learning and problem solving. The fact that clarifying learners’ ideas was highest indicated the much problems and complaints pupils faced. While the highest in cooperative learning and problem solving showed how much pupils interacted with one another in groups during mathematical problem solving but less of teacher’s prompts to clarify value judgements on their strength and weaknesses. Furthermore, pupils used problem-solving activities more frequently indicated the extent cognitive processes were over- emphasized as opposed to them working simultaneously with the metacognitive processes.  Pupils used least journal keeping, evaluating ways of thinking, planning strategy and identifying difficulty, which was a good indication that they could not use metacognitive strategies to record, set their own goals, assess their own thinking and be supported according to their individual needs. These results point that a teacher has to find ways of making mathematical concepts available to learners so that learning creates a metacognitive environment where mathematical authority empowers the learners’ mathematical work to indulge in metacognitive strategies useful during lessons and their studies. Keywords: mathematical problem solving, metacognitive skills, Metacognitive strategies, mathematical authority. DOI: 10.7176/JEP/10-15-15 Publication date:May 31st 201

Isolation and identification of feruloylated arabinoxylan mono- and oligosaccharides from undigested and digested maize and wheat

AbstractFeruloylated arabinoxylan mono- and oligosaccharides (F-AXOS) are a subject of interest because of their prebiotic and antioxidant properties. We aimed at isolating and identifying F-AXOS from maize, wheat, wheat bran and wheat aleurone using HPLC and LC-MS/MS. Prior to extraction of F-AXOS, samples were subjected to either simulated gastric fluid with enzymes (gastric) or without enzymes (pH) or water (aqueous) at 37 °C. F-AXOS present in all samples were identified as 5-O-feruloyl-α-L- arabinofuranose and possibly 5-O-feruloyl-α-L-arabinofuranosyl-(1 → 3)-O-β-D-xylopyranose. Their mean content, measured as esterified ferulic acid (FA), was 2.5 times higher in maize (10.33 ± 2.40 μg/g) compared to wheat. Digestion under gastric or pH conditions resulted in a two-fold increase in F-AXOS in all samples. The level of F-AXOS produced during gastric or pH condition was positively correlated to the insoluble bound FA content of the sample (R2 = 0.98). 5-O-Feruloyl-α-L- arabinofuranose was the only identifiable F-AXOS released during gastric digestion. Our results suggest feruloyl arabinose is the most abundant form of F-AXOS in maize and wheat

Study of yield stability and breeding for common bacterial blight resistance in SOuth African dry bean germplasm.

Master of Agriculture in Plant Breeding. University of KwaZulu-Natal, Pietermaritzburg 2016.Abstract available in PDF file