Text Review Research Articles

Twitter sentiment analysis for Hausa abbreviations and acronyms

The use of natural language processing, to identify, extract and organize sentiment from user generated texts in social networks, blogs or product review of text is known as sentiment analysis or opinion mining. Hausa language belongs to one of the major well-spoken languages in Africa and one of the three major Nigerian languages. Now investigating into such a language will have significant influence on social, economic business political and even educational services and settings. Some of these Hausa texts are abbreviated and some in acronym format which is a challenge to researchers as such comments are in an unstructured format and needs normalization to get further understanding of that text and also there is scarcity of sentiment analysis on Hausa abbreviation and acronym. Abbreviation is a shorten form of a word while acronym is an abbreviation formed from the initial letters of other words and pronounced as a word. This research aims to develop an improved Hausa Sentiment Dataset for the enhancement of sentiment analysis with abbreviation and acronyms. This is achieved by adapting to the approach for Hausa Sentiment Analysis based on Multinomial Naïve Bayes (MNB) and Logistic Regression algorithms using the count vectorizer, along with python libraries for NLP. This research affirmed that the improved dataset with abbreviation and acronym outperforms the plain Hausa dataset by 4% in accuracy using Multinomial Naïve Bayes. The result shows that in addition to normal preprocessing techniques of the social media stream, understanding, interpreting and resolving ambiguity in the usage of abbreviations and acronyms lead to improved accuracy of algorithms with evidence in the experimental result.

Stress reduction and psychological therapy for IBS: a scoping review

IntroductionIrritable Bowel Syndrome (IBS) is a highly prevalent functional gastrointestinal disease that is commonly associated with psychological comorbidities and maladaptive thought patterns. Previous studies report psychological therapies such as cognitive behavioral therapy (CBT) and gut-directed therapy (GDP) improve IBS symptom management and quality of life. This review seeks to understand the effectiveness of various psychotherapies across delivery methods for patients with irritable bowel syndrome.MethodsA scoping literature review of PubMed articles highlighting psychological and stress reduction treatments for IBS was conducted. 120 studies were included in the title and abstract screening. 32 studies were selected for full text review. Primary and secondary research studies that investigated the benefit of psychological therapies focusing on stress reduction and cognitive therapies for patients with gastrointestinal condition’s symptom relief met inclusion criteria for the review. 12 studies were selected for inclusion.ResultsAll 12 reviewed studies reported statistically significant improvements in IBS symptoms with psychological therapies. 8 studies also addressed quality of life and reported statistically significant improvement in intervention groups. 3 studies demonstrated persistent improvement after 12 months. 2 studies compared different types of psychotherapies and reported improvements compared to control groups but no significant differences between psychotherapies. 6 studies that compared face to face therapy with minimal contact or telephone therapy showed no difference in clinical outcomes.DiscussionPsychological therapies demonstrate reported statistically significant improvements in IBS symptoms and patient quality of life with no reported statistically significant difference across forms of healthcare delivery. Most improvements reportedly persist long-term. Further research with a broader demographic base is needed to assess the economic costs of psychological therapies and their implications for underserved communities.

Scalable deep learning framework for sentiment analysis prediction for online movie reviews

Sentiment analysis has broad use in diverse real-world contexts, particularly in the online movie industry and other e-commerce platforms. The main objective of our work is to examine the word information order and analyze the content of texts by exploring the hidden meanings of words in online movie text reviews. This study presents an enhanced method of representing text and computationally feasible deep learning models, namely the PEW-MCAB model. The methodology categorizes sentiments by considering the full written text as a unified piece. The feature vector representation is processed using an enhanced text representation called Positional embedding and pretrained Glove Embedding Vector (PEW). The learning of these features is achieved by inculcating a multichannel convolutional neural network (MCNN), which is subsequently integrated into an Attention-based Bidirectional Long Short-Term Memory (AB) model. This experiment examines the positive and negative of online movie textual reviews. Four datasets were used to evaluate the model. When tested on the IMDB, MR (2002), MRC (2004), and MR (2005) datasets, the (PEW-MCAB) algorithm attained accuracy rates of 90.3%, 84.1%, 85.9%, and 87.1%, respectively, in the experimental setting. When implemented in practical settings, the proposed structure shows a great deal of promise for efficacy and competitiveness.

Patient preferences for prophylactic regimens requiring regular injections in children and adolescents: a systematic review and thematic analysis

BackgroundAt present, limited literature exists exploring patient preferences for prophylactic treatment of acute rheumatic fever (ARF) and rheumatic heart disease (RHD). Given low treatment completion rates to this treatment in...

Концепцията на Такис Папас за популизма като демократичен илиберализъм

Abstract: The article discusses Takis Pappas’s concept of populism as democratic illiberalism. The method of desk research is used and a critical review and reading of the main scholarly texts by Takis Papas concerning the tension between populism and liberal democracy. The analysis has highlighted three main elements of this concept: first, the distinction between liberal and populist democracy; second, the role of charismatic leadership; third, the causal mechanisms that cause populism to emerge and stay in power (politicization of public discontent, creation of a new cleavage between “the people” and “the elite”, and deepening of political polarization). The analysis also includes examples from contemporary political realities in different countries. Conclusions are also drawn on Takis Pappas’s contribution in terms of research methodology in bringing out empirical charac¬teristics of populism, which are related to conceptual unfolding of its definition as well as its relevance in contemporary politics. Conclusions regarding the units of analysis, the conceptual scope of the concept and its specificities are presented. Keywords: Takis Pappas, populism, democratic illiberalism, populist democracy, causal mechanisms

TEXT Reviews April 2024

Dominique Hecq reviews Julia Prendergast, Eileen Herbert-Goodall and Jen Webb’s (eds.) The Writing Mind: Creative Writing Responses to Images of the Living Brain; Verity Oswin reviews John Kinsellas’ Legibility: An Antifascist Poetics; Amelia Walker reviews Mathelinda Nabugodi’s Shelley with Benjamin: A critical mosaic; Aidan Coleman reviews Ada Calhoun’s Also a Poet; and Verity Oswin reviews Adelle Sefton-Rowston’s Polities and Poetics – Race Relations and Reconciliation in Australian Literature.

EFL Pre-Service Teachers’ Experiences in Speaking through a Digital Storytelling Project

This study delves into the experiences of EFL pre-service teachers engaging in a digital storytelling project to practice their speaking, with a focus on exploring significant learning outcomes through Fink’s 2003 theory. This qualitative case study utilized semi-structured interviews and project reports as instruments, collecting data from five EFL pre-service teachers at Mulawarman University Samarinda through purposeful sampling. Thematic analysis was employed to analyze the data. Four major themes emerged from the data's thematic analysis, which encompassed the experiences of the participants: participation in the process of creating digital stories, focused speaking exercises, integration of other language abilities, and self-evaluation techniques. These themes also brought attention to the participants' learnings about digital storytelling for public speaking, the difficulties they faced, and the spectrum of emotions they went through. Furthermore, the significant learning outcomes they experienced were marked by an improvement in speaking skills and the establishment of speaking habits. Participants also gained insight into the relationship between activities such as writing scripts, recording, and reading for text reviews, all of which contributed to practicing their speaking. This project fosters self-awareness, enhances understanding of others, and instils an interest in undertaking similar projects in the future. Demonstrated ongoing learning highlights the impact of digital storytelling beyond technical skills, serving as a valuable tool for speaking practice.

Deep learning-based method for sentiment analysis for patients' drug reviews.

This article explores the application of deep learning techniques for sentiment analysis of patients' drug reviews. The main focus is to evaluate the effectiveness of bidirectional long-short-term memory (LSTM) and a hybrid model (bidirectional LSTM-CNN) for sentiment classification based on the entire review text, medical conditions, and rating scores. This study also investigates the impact of using GloVe word embeddings on the model's performance. Two different drug review datasets were used to train and test the models. The proposed methodology involves the implementation and evaluation of both deep learning models with the GloVe word embeddings for sentiment analysis of drug reviews. The experimental results indicate that Model A (Bi-LSTM-CNN) achieved an accuracy of 96% and Model B (Bi-LSTM-CNN) performs consistently at 87% for accuracy. Notably, the incorporation of GloVe word representations improves the overall performance of the models, as supported by Cohen's Kappa coefficient, indicating a high level of agreement. These findings showed the efficacy of deep learning-based approaches, particularly bidirectional LSTM and bidirectional LSTM-CNN, for sentiment analysis of patients' drug reviews.

Implementation of Multimedia Project-Based Learning Model and Language Attitude towards Writing Skills

The implementation of learning skills in writing review texts is still oriented toward completing the tasks given by the teacher. This results in students needing help developing review text writing skills. This research examines how implementing a multimedia-based Project-Based Learning learning model and language attitudes affects students' review text writing skills in class VII SMP. Classified as a type of quantitative research, this research applies an experimental method with a 2×2 factorial design. The sample was taken from the entire population, namely class VII.1 was chosen as the experimental class, and class VII.2 was chosen as the control class with 60 students. Based on the analysis and discussion results, the multimedia-based project-based learning model is proven to improve learning outcomes for students' review of text writing skills compared to conventional learning models. The multimedia-based project-based learning model provides a basis for reflecting on effective teaching methods. These results indicate that involving students in multimedia projects can stimulate creativity and strengthen their understanding of writing concepts. Apart from that, this study's results confirm no interaction between the multimedia-based Project-based Learning learning model and language attitudes.

Holding and restraining children for clinical procedures: A scoping review of health professional reported and observed practice.

Holding and restraining children during non-urgent clinical procedures continues to be surrounded by uncertainty and mired in controversy. This review aimed to locate, appraise and map the evidence related to health professionals reported and observed practice of holding and restraining children, from birth to 16 years, for clinical procedures. This scoping review, conducted in April 2022, was based on the Joanna Briggs Institute protocol. Screening and full text review resulted in the inclusion of 30 papers. In total, 14 different terms were used to refer to the act of holding or restraining a child for a procedure, in many papers the action of holding was not defined. Professionals report the main factors influencing their decisions to use restraint and/or holding were the age of a child, with younger children being restrained or held most frequently; a child's behaviour; and concerns around a child's safety. Professionals also report that they can perceive pressure from parent/carers to hold or restrain their child and describe how holding practices can be influenced by service and organisational pressures. Health professionals, mainly nurses, continue to report ethical and moral tensions linked to their involvement in the restraint or holding of a child against their will for a clinical procedure. Evidence indicates a need to move practice forward as the issues identified in paediatric practice are long-standing and historical.

What makes a better hotel selection? A hybrid approach considering tourists’ preference and review helpfulness

ABSTRACT Hotel selection is crucial in planning trips. Several studies constructed the decision methods, which assumed that people are completely rational and neglected decision-maker’s (DM’s) behavioural aspects, based on text reviews or numerical ratings with the subjective or objective weights of criteria. Therefore, this study proposes a picture fuzzy gained and lost dominance score (PF-GLDS) method based on prospect theory, which integrates user-generated text reviews and multi-criteria ratings to facilitate hotel selection while considering tourists’ preference and review helpfulness. First, review helpfulness is analyzed to enhance data quality. Second, a decision matrix is constructed by simultaneously considering sentiment in helpful text reviews as well as multi-criteria ratings. Third, the criteria are derived by the maximising deviation and best worst methods (BWM), respectively, for tourists’ preferred objective and subjective weights. Finally, to address hotel selection problems, an extended GLDS method with picture fuzzy information, considering bounded rationality behaviour, is developed. Empirical evaluation on five hotels from the TripAdvisor website demonstrates the reasonableness and advantages of our proposed hybrid approach in achieving more objective and reliable decision making. Concurrently, this study can serve as a reference for tourists seeking well-suited hotels and tourism website optimisation.

Early Life Exposures and the Development of the Infant Gut Microbiome: A Review

The influence of the intestinal microbiota on metabolic, nutritional, and immunological processes is widely reported. With increasing literature associating altered microbial compositions with adverse health outcomes, it is important to understand how early life exposures may impact the development of gut microbial colonization and subsequent risk of altered metabolic and immune regulation. The purpose of this review is to describe factors in the maternal prenatal and perinatal period that may impact the intestinal microbiome over the first 2 years of life. A comprehensive search of MEDLINE, EMBASE, and the Cochrane Library using Medical Subject Headings (MeSH) and keywords for studies reporting on determinants of gut microbiota in infants (0 to 24 months) born at full term was conducted. Articles using culture techniques were included but not those that exclusively used molecular techniques that lacked sensitivity. Each citation title and abstract was independently assessed for inclusion for full text review. Findings related to the maternal prenatal period, mode of birthing, infant diet and antibiotics were included. Bifidobacteria and Bacteroides abundance were consistently greater in the first weeks of life in children born vaginally, and increased Bacteroides presence persisted throughout the first year. Bifidobacteria abundance was greater in breastfed children. Introduction of solid food was associated with greater presence of bacteria of the Firmicutes phylum. Although these studies advance our knowledge of how exposures in prenatal, intrapartum, and early life may impact colonization, larger studies with longitudinal follow-up are needed to improve our understanding of how perturbations may contribute to early origins of disease. This article has been peer reviewed.

Buddhist Higher Learning Horizon and Its Relation to Global Trends : The Trajectory of Global Buddhist Pedagogy

This study aims to dissect the challenges, identify the unique characteristics, and propose viable strategies for the seamless integration of local and global dimensions in higher Buddhist education. This study used the systematic literature review method with a qualitative approach. The research hinged on an extensive review of scholarly articles, primary texts, and reports on Buddhist education in Indonesia and internationally and then analyzed using thematic analysis. Results highlight that Indonesian Buddhist education possesses unique characteristics and mirrors global patterns, albeit with localized interpretations. The study underscored the importance of recognizing and integrating the Buddhayana spirit amidst global Buddhist sectarian diversities. The contribution of this review was twofold: first, it enriches the understanding of the specificities of Indonesian Buddhist higher education and its relation to global trends; second, it advocates for a harmonious blend of local and global in shaping the trajectory of global Buddhist pedagogy.

Research on Image Perception of Tourist Destinations Based on the BERT-BiLSTM-CNN-Attention Model

Image perception of tourism destinations plays an important role in destination marketing and management. Considering that long-distance feature information of travel review text is difficult to capture and local key information is ignored, BiLSTM and CNN are improved to propose a travel text classification method based on BERT-BiLSTM-CNN-Attention hybrid neural network model. Taking Sanya City as the research object, we adopt the emotion classification and content analysis methods and construct the tourism destination image perception analysis framework based on the “cognitive-emotional” three-dimensional model, providing a research perspective for the sustainable development of tourism in Sanya City. The results show that the accuracy of the proposed model reaches 93.18%, which is better than other models. Tourists’ perception of destination image includes four aspects: tourism resources, tourism environment perception, tourism infrastructure and supporting services, as well as tourism activities. Positive emotions dominate emotional image, and negative emotions are mainly focused on tourism infrastructure and supporting services. On the overall image perception, tourists have a high evaluation of the tourism image of Sanya City. This research has some implications for tourism destinations, such as improving their management programs, enhancing their marketing strategies, and achieving long-term sustainable development of their destinations.

A framework for decision making to purchase the best product using feature-based opinions

As more people express their thoughts on products on various online shopping platforms, the feelings expressed in these opinions are becoming a significant source of information for marketers and buyers. These opinions have a big impact on consumers’ decision to buy the best quality product. When there are too many features or a small number of records to analyze, the decision-making process gets difficult. A recent stream of study has used the conventional quantitative star score ratings and textual content reviews in this context. In this research, a decision-making framework is proposed that relies on feature-based opinions to analyze the textual content of reviews and classify buyer’s opinions, thereby assisting consumers in making long-term purchases. The framework is proposed in this paper for product purchase decision making based on feature-based opinions and deep learning. Framework consists of four components: i) Pre-processing, ii) Feature extraction, iii) Feature-based opinion classification, and iv) Decision-making. Web scraping is used to obtain the dataset of Smartphone reviews, which is subsequently clean and pre-processed using tokenization and POS tagging. From the tagged dataset, noun labeled words are retrieved, and then the probable product’s features are extracted. These feature-based sentences or reviews are processed using a word embedding to generate review vectors that identify contextual information. These word vectors are used to construct hidden vectors at the word and sentence levels using a hierarchical attention method. With respect to each feature, reviews are divided into five classes: extremely positive, positive, extremely negative, negative, and neutral. The proposed method may readily detect a customer’s opinion on the quality of a product based on a certain attribute, which is beneficial in making a purchase choice.

Overcoming the semantic gap in the customer-to-manufacturer (C2M) platform: A soft prompts-based approach with pretrained language models

Customer-to-Manufacturer (C2M) is a strategy in smart manufacturing where customers collaborate with manufacturers for customized product development on an online platform. The platform enables the shift from the traditional manufacturing process, which is driven by research and marketing, toward a customer-centric product development process. However, a challenge arises as customers lack technical knowledge to communicate their product specifications effectively, creating a semantic gap. This paper proposes a soft prompt-based network structure that utilizes pretrained language models to bridge the semantic gap on the C2M platform. To address limited customer needs data and imbalanced classes, a large corpus of product review texts is used to establish a mapping between reviews and product specifications. A smaller set of customer needs text is then employed to adapt this mapping to the target customer needs-product specifications relationship, thereby closing the semantic gap. The experimental results demonstrate the effectiveness of the proposed model adaptation operation and the prompting structure. Additionally, the experiments highlight the robustness of the proposed method against variations in training data size, thereby mitigating the challenges posed by imbalanced classes. The proposed method could potentially bring innovation to product customization and C2M platform development. By bridging the semantic gap, companies can better integrate customers in the co-design process and effectively translate customer needs into actionable product specifications.

Grounded Theory Analysis of Online Shopping Reviews Based on Text Clustering

Grounded Theory Analysis of online shopping reviews can improve the quality and accuracy of review data analysis, providing valuable information and suggestions for relevant businesses and consumers. This article focuses on analyzing mobile phone reviews on JD.com as coding objects, effectively employing machine learning algorithms such as Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), and text clustering in natural language processing to extract thematic keywords from online shopping review texts and perform axial coding. Subsequently, DBSCAN clustering algorithm is applied to categorize the main categories of design, service, and production under the perspective of businesses and consumers as consumption attraction, while categorizing price and brand under consumption intention, forming selective coding. By applying Grounded Theory to the analysis of online shopping reviews, this study accurately identifies consumer needs and feedback, with the aim of helping businesses improve product and service quality, enhance customer satisfaction and loyalty, and engage in personalized marketing and customized services.

Assessing Healthcare Simulation Facilitation: A Scoping Review of Available Tools, Validity Evidence, and Context Suitability for Faculty Development in Low-Resource Settings.

Assessment tools support simulation facilitation skill development by guiding practice, structuring feedback, and promoting reflective learning among educators. This scoping review followed a systematic process to identify facilitation assessment tools used in postlicensure healthcare simulation. Secondary objectives included mapping of the validity evidence to support their use and a critical appraisal of their suitability for simulation faculty development in low-resource settings. Database searching, gray literature searching, and stakeholder engagement identified 11,568 sources for screening, of which 72 met criteria for full text review. Thirty sources met inclusion; 16 unique tools were identified. Tools exclusively originated from simulation practice in high-resource settings and predominantly focused on debriefing. Many tools have limited validity evidence supporting their use. In particular, the validity evidence supporting the extrapolation and implications of assessment is lacking. No current tool has high context suitability for use in low-resource settings.

Role of tPA in Aphasia Recovery: A Meta-Analysis of Recent Studies

ABSTRACT Background Tissue plasminogen activator (tPA) is a common therapeutic agent approved to treat patients with stroke. When the agent is administered within 4.5 hours of stroke onset, tPA administration helps to restore blood flow to brain regions affected by a stroke, thereby limiting the risk of damage and functional impairment. However, research evaluating the use of tPA on aphasia – loss of the ability to understand or express speech – a common post-stroke condition is limited. A meta-analysis of available evidence was performed to assess the association between the use of tPA on aphasia post-stroke. Methods A literature search was performed using the PubMed, Web of Science, and SCOPUS electronic database using keywords “tPA”, “tissue plasminogen activator”, “aphasia”, and “stroke” published in English since 2010. An initial search revealed 7, 069, 6,682, and 289 studies in PubMed, Web of Science, and SCOPUS, respectively. Studies without a measure of aphasia impairment in adults as well as case studies and reviews were excluded. After quality assessment and a review of study abstracts, 14 relevant studies were identified for full text review. Among the 14, ten were deemed appropriate for inclusion and feature extraction. Results Associations between tPA and aphasia outcomes in each of the included studies were translated to odds ratios (ORs) and study features were coded for inclusion in a random effects meta regression. Results revealed a statistically significantly higher likelihood of improvement in the level of post-stroke aphasia among those receiving tPA compared with those not receiving tPA (odds ratio [OR], 2.1963, CI = 0.5994, 3.7932). Model statistics did however indicate substantial heterogeneity (I2 = 96.94%, Ƭ2 = 32.65) between studies. After removal of two outliers from the analysis set, the estimates improved substantially (OR = 1.8643, CI = 1.2131, 2.5155), and heterogeneity was reduced (I2 = 76.28%, Ƭ2 = 4.22). Estimates were robust to the inclusion of study location, metric used to measure aphasia, observed recovery duration, and size of the sample. Conclusion This meta-analysis found an increased likelihood of aphasia recovery in patients receiving tPA following stroke and significant improvement in communication from tPA receipt. However, further prospective studies with greater sample heterogeneity are needed to determine the appropriate window for tPA use, the duration of improvement, and the long-term effects on communication outcomes.

A rapid review of differences in cerebrospinal neurofilament light levels in clinical subtypes of progressive multiple sclerosis.

Multiple sclerosis (MS) is divided into three clinical phenotypes: relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), and primary progressive MS (PPMS). It is unknown to what extent SPMS and PPMS pathophysiology share inflammatory or neurodegenerative pathological processes. Cerebrospinal (CSF) neurofilament light (NfL) has been broadly studied in different MS phenotypes and is a candidate biomarker for comparing MS subtypes. Are CSF NfL levels different among clinical subtypes of progressive MS? A search strategy identifying original research investigating fluid neurodegenerative biomarkers in progressive forms of MS between 2010 and 2022 was applied to Medline. Identified articles underwent title and abstract screen and full text review against pre-specified criteria. Data abstraction was limited to studies that measured NfL levels in the CSF. Reported statistical comparisons of NfL levels between clinical phenotypes were abstracted qualitatively. 18 studies that focused on investigating direct comparisons of CSF NfL from people with MS were included in the final report. We found NfL levels were typically reported to be higher in relapsing and progressive MS compared to healthy controls. Notably, higher NfL levels were not clearly associated with progressive MS subtypes when compared to relapsing MS, and there was no observed difference in NfL levels between PPMS and SPMS in articles that separately assessed these phenotypes. CSF NfL levels distinguish individuals with MS from healthy controls but do not differentiate MS subtypes. Broad biological phenotyping is needed to overcome limitations of current clinical phenotyping and improve biomarker translatability to decision-making in the clinic.