Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media

Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in IJCVR on September 201

The Capability of E-reviews in Online Shopping. Integration of the PLS- SEM and ANN Method

Purpose: The aim of this study is to investigate the impact of e-review on iGen's propensity to purchase online. Especially, it can be better understood by dissecting the relationship among 3 variables of e-review (review valence, quantity of e-review and quality of e-review), e-satisfaction, and intention to buy.   Theoretical framework: This study classifies e-reviews according to their valence, quantity, and quality based on the study of Khammash (2008).   Design/methodology/approach: The PLS-SEM method was used to analyze data collected from online surveys administered to a sample of 222 iGen in Ho Chi Minh City to assess the hypotheses behind the study. Additionally, the Artificial Neural Network technique was used to separate SEM predictors that were relatively important.   Findings:  There are three results from the investigation: It has been found that (1) e-satisfaction is positively affected by valence, (2) e-satisfaction is generally increased with the high quality of e-review, but the quantity of e-review does not necessarily affect customers' e-satisfaction, and (3) e-satisfaction given in the context of an e-commerce platform has a strong effect on customers' online shopping intention. This study sheds new light on iGen's online buying habits and offers valuable management implications for iGen, online merchants, and e-commerce sites.   Research, Practical & Social implications:  E-reviews have become a significant factor in determining consumers' online purchase decisions. They also assist iGen in understanding how a qualified e-review—one that is clear, understandable, helpful, and has enough justification to support the opinions—will be advantageous for other consumers who wish to shop online.   Originality/value:  Provides the theory of e-review and its role in the online business environment. In addition, understand more about the behavior of igen, an age with a huge amount of spending on an online shopping platform

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Handling negative mentions on social media channels using deep learning

Social media channels such as social networks, forum or online blogs have been emerging as major sources from which brands can gather user opinions about their products, especially the negative mentions. This kind of task, popular known as sentiment analysis, has been addressed recently by many deep learning approaches. However, negative mentions on social media have their own language characteristics which require certain adaptation and improvement from existing works for better performance. In this paper, we propose a new architecture for handling negative mentions on social media channels. As compared to the architecture published in our previous work, we expose substantial change in the combination manner of deep neural network layers for better training and classification performance on social-oriented messages. We also propose the way to re-train the pre-trained embedded words for better reflect sentiment terms, introducing the resultant sentimentally-embedded word vectors. Finally, we introduce the concept of a penalty matrix which incurs more reasonable loss function when handling negative mentions. Our experiments on real datasets demonstrated significant improvement

Antiproliferative, Anti-Inflammatory Activities, and Molecular Docking Studies of Secondary Metabolites from Macrosolen tricolor

In Vietnam, Macrosolen tricolor is used for the treatment of bloating, broken bones, cough, diarrhea, diuretic, rheumatism, and laxative effects. The study aimed to identify the in vitro antiproliferation and anti-inflammation of all fractions and purified compounds from the M. tricolor whole plants, as well as the in silico molecular docking of the potentially cytotoxic compounds. As the results, fractions (MTH.I, MTH.II, MTE.I, and MTE.II) strongly demonstrated antiproliferative properties against three tested cells, MDA-MB-231, RD, and HepG2 (IC50 values ranged from 4.00 ± 0.20 to 70.60 ± 1.44 μg/mL), as well as anti-inflammatory effects (IC50 values ranged from 4.45 ± 0.08 to 23.00 ± 1.18 μg/mL), whereas other fractions meaningfully evidenced selective cytotoxicity and/or anti-inflammation. Therefore, the phytochemical compositions of the active fractions were illuminated, leading to the characterization of eighteen compounds. Compounds (3–5) revealed the most cytotoxic effects towards all examined cells (IC50 values ranged from 6.88 ± 0.12 to 71.64 ± 1.17 μM) and the strongest anti-inflammatory properties (IC50 values of 16.30 ± 0.92, 7.31 ± 0.55, and 9.23 ± 0.60 μM, respectively). Compound 11 showed potential cytotoxicity against MDA-MB-231, RD, and HepG2 cells (IC50 values of 24.42 ± 0.28, 20.60 ± 0.25, and 3.20 ± 0.02 μM, respectively). Furthermore, compounds (4, 5, and 11) interacted with the active site of the apoptosis regulator Bcl-2 protein (PDB ID: 2O2F), were comparable to PAC, and were compatible with their anticancer activity. This project suggests that M. tricolor is a good source of natural antiproliferative and anti-inflammatory agents and contributes to understanding the biological activities of Macrosolen species in traditional Vietnamese medicine

Vietnam's Political Economy: A Discussion on the 1986-2016 Period

Being a member of the thriving ASEAN and successfully implementing economic renovation (Doi Moi) have drawn the world's attention on Vietnam around the turn of the millennium. Some even expected a much faster pace of transformation, and renewed economic, AND political, reforms in Vietnam, or Doi Moi II.However, in the recent transition turmoil the Vietnamese economy has experienced some significant setback, and the solution for getting the country out of the downward spiral of low productivity, waning purchasing power and increasing costs of doing business cannot be worked out without addressing those political economy issues that have shaped the modus operandi of the nation's economic system. This article discusses the post-Doi Moi political economy in Vietnam, from 1986 to 2016 – when the 12th Congress of the Communist Party of Vietnam takes place – and prospects of reviving reform momentum in subsequent years.info:eu-repo/semantics/publishe

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BackgroundEstimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period.Methods22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.FindingsGlobal all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.InterpretationGlobal adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic