Relevant Words Research Articles

Sentiment analysis: A survey on design framework, applications and future scopes.

Sentiment analysis is a solution that enables the extraction of a summarized opinion or minute sentimental details regarding any topic or context from a voluminous source of data. Even though several research papers address various sentiment analysis methods, implementations, and algorithms, a paper that includes a thorough analysis of the process for developing an efficient sentiment analysis model is highly desirable. Various factors such as extraction of relevant sentimental words, proper classification of sentiments, dataset, data cleansing, etc. heavily influence the performance of a sentiment analysis model. This survey presents a systematic and in-depth knowledge of different techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The paper performs a critical assessment of different modules of a sentiment analysis framework while discussing various shortcomings associated with the existing methods or systems. The paper proposes potential multidisciplinary application areas of sentiment analysis based on the contents of data and provides prospective research directions.

An innovative automatic indexing method for Arabic text

Automatic indexing and text retrieval methods for languages have been studied for a long time. Automatic indexing is a process of extracting words from a document to classify the documents per subject and to enhance the information retrieval process. Compared to other languages, there is still limited research conducted for automated Arabic text categorization. In this work, we present an innovative method to reinforce the accuracy of automatic indexing of Arabic texts by introducing and integrating a thesaurus. Our model extracts new relevant words by referring to the created thesaurus, which contains and identifies words, synonyms, and correlations. This thesaurus is built using a natural language toolkit, which contains a library that lists the synonyms of a particular word available in the WordNet library. The words that have the same meaning and frequently appear together are grouped under one umbrella using a JavaScript Object Notation dictionary, making it leisurely to identify the topic of the text. Our results exhibit notable improvement in accuracy and efficiency compared to previous works.

Association between congenital heart disease and autism spectrum disorders: A protocol for a systematic review and meta-analysis.

Congenital heart disease (CHD), the most common heart defect in children, refers to congenital disease with abnormal development of the heart or large blood vessels during the fetal period. The researchers suggest that children with CHD show more obvious neurodevelopmental disorders than children with normal development, and children with CHD may have a higher risk of social interaction and communication disorders. This is similar to the characteristics of children with autism spectrum disorder (ASD). However, the association between type of CHD and ASD is not well understood. This systematic review and meta-analysis will reveal the relationship between type of CHD and ASD. We will search the Cochrane Library, Embase, PubMed, China National Knowledge Infrastructure, Wanfang, Chinese Scientific Journals Full text, and China Biology Medicine disc databases using relevant subject terms and free words. We will use a fixed effects model or random effects model for meta-analysis. The risk of bias will be assessed by the Newcastle-Ottawa Scale and the agency for health care research and quality. Heterogeneity will be tested by Q statistics and I² values. Publication bias will be detected by funnel plots and Egger test. Subgroup analyses and sensitivity analyses will also be used to explore and interpret the heterogeneity. The study will afford additional insight into the investigation the association between type of CHD and ASD. The results will provide evidence for the early identification and early intervention of ASD in children with CHD, which may contribute to improving the neurodevelopmental outcome of children with CHD.

Sino-Japanese Contrast and Translation of Emergency Language—Take COVID-19 for Example

Emergency language service is an important component of disaster management, which runs through the whole process of disaster from occurrence to solution. As a disaster-prone area, Japan has rich practical experience in emergency language services. The context formed by major emergencies often endows existing words with certain relevance semantics or situational semantics, as well as the birth to some new words. On the basis of expounding the formal characteristics and operating regulations of emergency language services in Japan, this paper analyzes and compares the changes and differences in relevant words in the context of the epidemic situation based on COVID-19. It is hoped that this study will provide some enlightenment for China to build emergency language service systems, so as to provide reference for the formulation of emergency language services in China.

ADS-Classif: A Robust Tool for Classifying Documents from Diverse Sources Using Weighted Domain-Specific Vocabulary

Extracting domain-specific words from the domain corpus to build vocabulary and identifying classes represented by domain-specific vocabulary is a challenging task in classifying text. Several comprehensive domain-specific techniques have been developed to build specific domain vocabulary but they lack generality and also do not consider the degree of the word's relevance. This paper proposes a supervised generic algorithmic model to create a domain-specific vocabulary. The model identifies domain-indicative word combinations from labeled real-time domain corpus, calculates their weights and logged their inclusion time to understand their semantic behavior in the present era of development. Further, a framework ADS-Classif is also developed using vocabulary to classify real-time documents extracted from different sources and test the domain-specific vocabulary's significance. Experimental results show that the approach leads to the accurate identification of domain classes for test documents compared with classical classifiers Naïve Bayes and Support Vector Machine.

Benefit-risk assessment of incretin and other anti-diabetic agents in type 2 diabetes using a stochastic multicriteria acceptability analysis model.

Benefit-risk assessment of incretin and other anti-diabetic agents in type 2 diabetes using a stochastic multicriteria acceptability analysis model.

Tonal properties in a non-tonal language: The case of Indonesian

This study investigates the tonal properties of Indonesian, specifically examining intonation contours in Indonesian at the sentence level and focusing on how the tonal system is used to indicate different pragmatic meanings in Indonesian. Four distinct intonation contours with four distinct meanings were contrasted: strong agreement to the truth of the relevant word (emphasizing) (H), interrogative meaning (LH), doubting the fact of the word in question (L), and a neutral conceptual meaning of the relevant word (HL). The stimuli for the study were four Indonesian words (two verbs makan ‘eat’ and tahu ‘know’ and two adjectives cantik ‘beautiful’ and marah ‘angry’). Stimuli were recorded to capture the four-way contrast in pitch contour. A comprehension task to identify the distinct meanings was conducted. Forty-nine participants were asked to listen to the four words with the four different pitch contours and select their respective meanings. The data show that the participants were able to apply the Mandarin four-way contrast pitch contour to Indonesian to accurately indicate four different (pragmatic) meanings. The most difficult contour for the listeners/participants was in distinguishing the interrogative intonation contour (LH) from the low-dipping intonation contour (L) signaling doubting the conceptual truth of the lexical items used. The overall study suggests that a tonal system based on Mandarin tonal contrasts can be applied to Indonesian intonational expressions.

Relative Importance of Sustainable Development Goals by Q-Sort Evaluation

The 17 Sustainable Development Goals offer a comprehensive framework for extensive development actions. The purpose of this study is to explore the patterns of perception of the relative importance of the goals. Although the interrelations between the diversified goals provide a comprehensive approach for decision-makers, the patterns may support effective strategies in the field. The study used a voluntary online survey among 123 business students by the Q-sort ranking method. The analysis identified three characteristic patterns for the relative importance of the goals: (1) global thinkers who prioritize planet-related goals; (2) pathfinders; and (3) human-centric students who prioritize people-related goals. The preference orders suggest the goals that can best improve the acceptance of sustainability actions. Climate change and water are the most relevant calling words for environmental actions, while health, hunger, and poverty are the equivalents for social actions. Collaboration by a partnership is not considered to be among the essential items, but the position of education is encouraging. Understanding the motivations of the respondents can be used for shaping attitudes in line with the policy expectations; moreover, the factor membership can be used as a grouping factor for a broader survey. The resultant factor characteristics can be used as grouping factors for broader surveys aimed at understanding the motivations behind environmentally conscious behavior.

Digital transformation under Covid-19: A Bibliometric Study and future research agenda

Digital transformation (DT) has been considered an essential enabler of operational and strategic change for any given organization across various industries. Based on the smart adoption and assimilation of cutting-edge tools and technologies, DT allows firms to develop new capabilities, including creating improved products and services, enhancing experiment and innovation abilities, becoming customers centric, and streamlining end-to-end business processes. No wonder some analysts believe that covid-19 has contributed to fostering DT trends across industries. Yet very few studies exist about the real impact of covid-19 on DT. This study aims at filling this knowledge gap using a bibliometric analysis of 441 documents collected from the Web of Science database. Two bibliometric tools, namely Bibliometrix and VOSViewer, were used for data analysis. It emerged that impact, innovation, and performance are among the top word growth and the most relevant words, while the top three relevant affiliations are based in the USA. Further insights are presented and discussed, followed by a future research agenda.

Stacking Deep-Learning Model, Stories and Drawing Properties for Automatic Scene Generation

Text-image mapping is of great interest to the scientific community, especially for educational purposes. It helps young learners, mainly those with learning difficulties, to better understand the content of stories. In this paper, we propose to capture the teacher’s experience in manually building relevant scenes for animal behavior stories. This manual work, which consists of a pair of texts and a set of elementary images, is fed into a Long Short-Term Memory (LSTM) followed by a Conditional Random Field (CRF) that aims to associate the relevant words in the text with their corresponding elementary image while preserving the drawing properties. This association is then used for scene construction. Several experiments were con-ducted to show how better the constructed scenes convey textual information than the scenes constructed from the competitor’s models.

Real-Time Prediction of Medical Demand and Mental Health Status in Ukraine under Russian Invasion Using Tweet Analysis.

Russia's invasion of Ukraine (February 24, 2022) has begun and there are concerns about the impact on health care supply and mental health. This study analyzed tweets in the Ukrainian language to capture the medical needs and mental health conditions in wartime Ukraine by focusing on ostensibly relevant words. The number of tweets containing the keywords and their overall proportion was compared before and after the Russian invasion of Ukraine. The survey period was divided into four phases-the pre-2022 Russian invasion, acute phase (4 weeks), subacute phase (12 weeks), and the chronic phase (8 weeks) up to August 10, 2022. The analysis targeted tweets sent in Ukrainian. The tweets were screened using a set of six classes with 75 key groups and 303 Ukrainian (204 original Japanese) keywords. Overall, 98,526,440 tweets were analyzed, with a pre-invasion and post-onset average of 1,096,976 and 3,328,243 tweets/week (a 3.0-fold increase), respectively. Of these, 3,197,443 tweets contained the keywords, with a pre-invasion and invasion average of 26,241 and 114,640 tweets/week (a 4.4-fold increase), respectively. The post-onset phase witnessed a considerable increase in all classes-medical services, treatment, medical resources, medical situations, and special situations-but not in the symptom class. Keywords related to psychological distress and anxiety immediately increased during the acute phase; those related to depression and post-traumatic stress reactions continued increasing as the invasion persisted, which may have reflected the mental state of those impacted. Analyzing tweets is useful for predicting people's real-time physical and mental health needs during wartime.

Bidirectional Long Short-Term Memory-Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches.

Social networking services (SNS) closely reflect the lives of individuals in modern society and generate large amounts of data. Previous studies have extracted drug information using relevant SNS data. In particular, it is important to detect adverse drug reactions (ADRs) early using drug surveillance systems. To this end, various deep learning methods have been used to analyze data in multiple languages in addition to English. A cautionary drug that can cause ADRs in older patients was selected, and Korean SNS data containing this drug information were collected. Based on this information, we aimed to develop a deep learning model that classifies drug ADR posts based on a recurrent neural network. In previous studies, ketoprofen, which has a high prescription frequency and, thus, was referred to the most in posts secured from SNS data, was selected as the target drug. Blog posts, café posts, and NAVER Q&A posts from 2005 to 2020 were collected from NAVER, a portal site containing drug-related information, and natural language processing techniques were applied to analyze data written in Korean. Posts containing highly relevant drug names and ADR word pairs were filtered through association analysis, and training data were generated through manual labeling tasks. Using the training data, an embedded layer of word2vec was formed, and a Bidirectional Long Short-Term Memory (Bi-LSTM) classification model was generated. Then, we evaluated the area under the curve with other machine learning models. In addition, the entire process was further verified using the nonsteroidal anti-inflammatory drug aceclofenac. Among the nonsteroidal anti-inflammatory drugs, Korean SNS posts containing information on ketoprofen and aceclofenac were secured, and the generic name lexicon, ADR lexicon, and Korean stop word lexicon were generated. In addition, to improve the accuracy of the classification model, an embedding layer was created considering the association between the drug name and the ADR word. In the ADR post classification test, ketoprofen and aceclofenac achieved 85% and 80% accuracy, respectively. Here, we propose a process for developing a model for classifying ADR posts using SNS data. After analyzing drug name-ADR patterns, we filtered high-quality data by extracting posts, including known ADR words based on the analysis. Based on these data, we developed a model that classifies ADR posts. This confirmed that a model that can leverage social data to monitor ADRs automatically is feasible.

PERSPECTIVES ON THE RISK FACTORS ASSOCIATED WITH MISSING INCIDENTS IN PERSONS LIVING WITH DEMENTIA

Abstract Persons living with dementia are at higher risk of getting lost and going missing. The adverse outcomes of missing incidents are stressful for persons living with dementia and those who care for them. This study aimed to identify and describe the perspectives of persons with dementia, caregivers and community support organizations on risk factors. Generic qualitative description informed our methods. We conducted 30 virtual interviews with persons who live with dementia, professional and family caregivers and community support organization representatives. We used a card sort to elicit and describe perspectives on the importance of 27 risk factors commonly associated with missing incidents in persons living with dementia. Interviews were digitally recorded, transcribed verbatim, and subjected to content analysis to determine the presence of relevant words, themes, and concepts. Participants reported multiple experiences of a person going missing, impressions, and suggested relationships between factors such as environmental contexts. The most critical risk factors associated with getting lost and going missing were cognitive impairment, unmet needs, and inadequate concentration of services and resources. In contrast, race, education, and gender were perceived as unimportant pertaining to risk factors related to missing incidents in persons living with dementia. An understanding of the perceived importance of risks associated with missing incidents enhances a person-centered approach to addressing unmet needs, services and resources that balances quality of life with maintaining safety.

Contextual Coding in Qualitative Research Involving Participants with Diverse Sociocultural Backgrounds

Understanding participants’ perspectives in qualitative research is contingent on unravelling the essential meaning of their speech. When data are collected in native language and translated into English language, the underlying sociocultural meaning of participants’ speech can be missed. This paper discusses a new contextual coding approach and illustrates its application in research. The technique was used in a phenomenological study in Pakistan and a mixed methods study in Europe. Contextual coding entails a preliminary coding stage involving data reading in native language, choosing socially and culturally relevant words and phrases, and developing preliminary codes. The concluding coding stage focuses on creating a sociocultural query list, seeking answers through discussions among multilingual individuals, and finding a common language for code description. Contextual coding can enable researchers to understand sociocultural meaning of their data at an early stage, rather than waiting at the later stage of theme development to contextualize the findings.

Standardized item selection for alternate computerized versions of Rey Auditory Verbal Learning Test(-based) word lists

ABSTRACT Introduction Despite an increasing need for new Rey Auditory Verbal Learning Test (RAVLT)-based word lists in computerized testing, no criteria or standardized procedures exist for its development. To lay a foundation for future development of new and alternate computerized RAVLT(-based) word lists, we present cross-lingual word criteria, developed new lists using the criteria and evaluated performance on the lists using online assessment. Method Based on psycholinguistic literature, we identified relevant word selection criteria. To validate the criteria, we developed two new American-English word lists and one new Dutch list, and administered the RAVLT using visual presentation of the new or original list in an online American (n = 248) and Dutch sample (n = 246) of healthy people. We compared performance of the new and original word lists on trial scores and serial position effects using Bayesian correlations and analyses of variance. Additionally, we compared proportions of correct responses per item, corrected for serial position. Results We identified 13 relevant word selection criteria. The criteria led to two new highly comparable American-English word lists with lower trial scores compared to the original American-English list, indicating that the criteria helped to develop parallel lists with fewer associations between items. The new Dutch word list showed similar trial scores, serial position effects, and proportions of correct responses per item corrected for serial position compared to the original Dutch version. Conclusions The systematic use of word selection criteria can facilitate development of new parallel word lists, including in new language areas. Future studies should evaluate the use of the word criteria for the other sections of the RAVLT (such as delayed recall and recognition), performance using original test modalities (auditory presentation and recall of words) as well as performance in clinical samples.

The effect of selective serotonin and norepinephrine reuptake inhibitors on clinical outcome of COVID-19 patients: A systematic review and meta-analysis.

Background and AimDue to the high social and economic burden and also mortality and morbidity caused by coronavirus disease 2019 (COVID‐19) in the past few years, researchers have aimed at finding solutions to suppressing the severity of infection. Recently, selective serotonin and serotonin‐norepinephrine reuptake inhibitors (SSRI/SNRI) have been investigated as an adjuvant treatment for COVID‐19. The aim of the current study was to investigate the impact of SSRI/SNRIs on outcomes of COVID‐19 patients.MethodsIn this systematic review and meta‐analysis, a comprehensive search strategy consisting of relevant words was performed by two researchers in PubMed, Scopus and EMBASE libraries. Studies reporting the effect of SSRI and/or SNRI use in COVID‐19 patients' outcome were included. Hospitalization, mortality, hospitalization event, and length of hospital stay were considered as main outcomes of this study. Analysis was carried out using Comprehensive Meta‐Analysis (CMA‐version 2) and final data were reported as odds ratio (OR) and 95% confidence interval (CI).ResultsOur search led to the final selection of 9 articles including 15,287 patients. The effect of fluvoxamine, fluoxetine, and the overall effect of SSRI/SNRI use on mortality of COVID‐19 patients were investigated in 3, 2, and 7 articles, respectively. The results of our analyses showed that these medications could significantly decrease mortality of COVID‐19 patients (OR and 95% [CI]: 0.595 [0.467–0.758], 0.620 [0.469–0.821], and 0.596 [0.437–0.813]). The effect of SSRI/SNRIs on hospitalization events of COVID‐19 patients was not significant (OR: 0.240% and 95% CI: 0.041–1.4). Also, length of hospital stay was longer in patients who administrated SSRIs.ConclusionAccording to this study's results, SSRI/SNRIs may be effective in reducing mortality of COVID‐19 patients, suggesting the superiority of fluvoxamine to fluoxetine. The safety profile and affordable cost of SSRI/SNRIs for a short‐term use may be other reasons to propose them as beneficial medications in preventing mortality in COVID‐19.

Geographic Space in Pentti Haanpää’s Novel Korpisotaa

Pentti Haanpää (1905-1955) was one of the most important Finnish authors in the first half of the 20th century. His short stories and novels describe life in the north-western part of the Finnish countryside many times, but his collected works also include many other themes. Among his works are five books, three novels, and two short story collections, which describe either military life or war. His first war novel, Korpisotaa describes the Finnish Winter War of 1939–40. Haanpää wrote the novel based loosely on his own war experiences for a competition for the best winter war novel arranged in 1940 by Prentice-Hall together with the Finnish publisher Otava; the novel was ranked third best in the competition. The novel is generally considered the first realistic war novel published in Finland [1-3], and its reception was favorable in general [4]. In this study, we focus on the analysis of geographic space in Korpisotaa. We use a digital version of the novel to be able to easily search for all the relevant space and location words in the novel. The methods we use in the study are familiar from linguistic corpus studies, and they have been used to some extent in literary studies as well. Besides common methods like keyness and frequency counts, we can benefit from a lexical semantic tagger of Finnish. Usage of the tagger systematizes the finding of the geographic space words in the novel and the comparison texts and enables us to perform keyness counts for semantic word groups instead of single words. Our work contributes especially to the use of digital methods in literary analysis and the creation of literary study corpora. Even for a novel-length, the availability of a digital version of the studied text helps detailed analysis very much, as will be shown in the analysis of Korpisotaa.

Learning words without trying: Daily second language podcasts support word-form learning in adults.

Spoken language contains overlapping patterns across different levels, from syllables to words to phrases. The discovery of these structures may be partially supported by statistical learning (SL), the unguided, automatic extraction of regularities from the environment through passive exposure. SL supports word learning in artificial language experiments, but few studies have examined whether it scales up to support natural language learning in adult second language learners. Here, adult English speakers (n = 70) listened to daily podcasts in either Italian or English for 2 weeks while going about their normal routines. To measure word knowledge, participants provided familiarity ratings of Italian words and nonwords both before and after the listening period. Critically, compared with English controls, Italian listeners significantly improved in their ability to discriminate Italian words and nonwords. These results suggest that unguided exposure to natural, foreign language speech supports the extraction of relevant word features and the development of nascent word forms. At a theoretical level, these findings indicate that SL may effectively scale up to support real-world language acquisition. These results also have important practical implications, suggesting that adult learners may be able to acquire relevant speech patterns and initial word forms simply by listening to the language. This form of learning can occur without explicit effort, formal instruction or focused study.

Emotion Analysis Based on Deep Learning With Application to Research on Development of Western Culture.

Cultural development is often reflected in the emotional expression of various cultural carriers, such as literary works, movies, etc. Therefore, the cultural development can be analyzed through emotion analysis of the text, so as to sort out its context and obtain its development dynamics. This paper proposes a text emotion analysis method based on deep learning. The traditional neural network model mainly deals with the classification task of short texts in the form of word vectors, which causes the model to rely too much on the accuracy of word segmentation. In addition, the short texts have the characteristics of short corpus and divergent features. A text emotion classification model combing the Bidirectional Encoder Representations from Transformers (BERT) and Bi-directional Long Short-Term Memory (BiLSTM) is developed in this work. First, the BERT model is used to convert the trained text into a word-based vector representation. Then, the generated word vector is employed as the input of the BiLSTM to obtain the semantic representation of the context of the relevant word. By adding random dropout, the mechanism prevents the model from overfitting. Finally, the extracted feature vector is input to the fully connected layer, and the emotion category to which the text belongs is calculated through the Softmax function. Experiments show that in processing short texts, the proposed model based on BERT-BiLSTM is more accurate and reliable than the traditional neural network model using word vectors. The proposed method has a better analysis effect on the development of western culture.

Category-learning attention mechanism for short text filtering

In machine translation, the attention mechanism highlights relevant words according to the distances between the source and target vectors dynamically. However, its ability to optimize text classification is limited because this mechanism only calculates weights inside the same text. There is an uneven distribution of words between ham (not spam) and spam categories, and this category-level feature has not previously been utilized in the attention mechanism for filtering short texts. In addition, short text filtering is uniquely challenging due to the length, sparsity and informal writing of texts, as well as a need for rapid processing. We propose a novel category-level attention mechanism called “category-learning attention,” which highlights words intensely distributed in the same category by dynamically calculating a category differentiation matrix for each short text. The category-learning attention mechanism is extended to the category-learning scaled-dot-product attention and the category-learning multi-head attention (CL-MHA) mechanisms. The CL-MHA mechanism is then applied to a bidirectional gate recurrent unit (Bi-GRU) model for performance evaluation by using the SMS spam collection dataset hosted at the University of California, Irvine. Performance metrics including the accuracy, precision, recall, and F1 score demonstrate that the CL-MHA mechanism significantly improves the performance of Bi-GRU for short text filtering with an accuracy of 99.35%, higher than any previously reported machine learning models. In addition, experiments conducted on three datasets - a Chinese SMS spam dataset, a benchmark movie review dataset, and a benchmark customer review dataset - further validate the effectiveness of the proposed model. The proposed CL-MHA Bi-GRU model has an accuracy of 99.46% when evaluated on the Chinese SMS spam dataset.