Amrita_student at SemEval-2018 Task 1: Distributed Representation of
            Social Media Text for Affects in Tweets

Unnithan, Nidhin A.; Kumari, Shalini; Ganesh, H B Barathi; Kumar, M. Anand; P, Soman K.

doi:10.18653/v1/s18-1047

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Families also reported lack of familiarity with health systems in urban areas especially when they had to locate services at a new location each time. These findings reflect similar experiences elsewhere, where households delay seeking healthcare for sick children because of distance or high out of pocket expenditure contributing further to morbidity and mortality [4, 62, 63].…”

Section: Discussionsupporting

confidence: 84%

Nutrition among children of migrant construction workers in Ahmedabad, India

Ravindranath

Trani

Iannotti

2019

Int J Equity Health

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Background Millions of poor households in India undertake short duration rural to urban migration along with their children to find work in the informal economy in the city. While literature has documented the precarity of such temporary jobs, typically characterized by low wages, insecure jobs, harsh recruitment regimes and economic vulnerability, little is known about its implications for children who migrate with their parents to the city. In this study, we draw attention to children of migrant construction workers and focus on their overall nutritional well-being, which remains under-studied. Our objectives were to categorize the current nutritional status of children under the age of five and determine the underlying causes of poor nutritional outcomes. Methods The field work for this study was undertaken between May 2017 and January 2018 at five construction sites in the city of Ahmedabad. We undertook anthropometric measurements of children under the age of five [N = 131; (male: 46%, female 53%); (mean age: 31.7 months)] and categorized their nutritional status. Using the UNICEF framework on undernutrition, we examined the underlying causes of poor nutritional outcomes among this group of children with the help of qualitative methods such as interviews, focused group discussions and participant observation at the field sites. Results Undernutrition was highly prevalent among the children (N = 131): stunted (40.5%); wasted (22.1%); and underweight (50.4%). We found common factors across parents interviewed such as similar misperceptions of malnutrition, long hours of work and lack of childcare provision at the worksite which resulted in disrupted quality of care. While socio-cultural beliefs and lack of information influenced breastfeeding, other factors such as inability to take breaks or lack of space further impaired infant feeding practices more broadly. Lack of dietary diversity at home, poor hygiene and sanitation, and economic inability to seek healthcare further affected child nutritional status. Conclusions Our study addresses a critical gap in migration literature in India by building a comprehensive narrative of migrant children’s experiences at construction sites. We find that parents’ informal work setting exposes children to a nutritionally challenging environment. Policies and programs seeking to address undernutrition, a critical challenge in India, must pay attention to the specific needs of migrant children.

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Section: Discussionsupporting

confidence: 84%

Nutrition among children of migrant construction workers in Ahmedabad, India

Ravindranath

Trani

Iannotti

2019

Int J Equity Health

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“…The brain tissue that experiences blood circulation disorders in first-time ischemic stroke patients is usually not too extensive compared to hemorrhagic strokes. Hemorrhagic stroke has the risk of causing more severe symptoms because of increased intracranial pressure, which worsens the oxygenation conditions of cerebral tissue (Unnithan et al, 2023). The more severe the stroke the patient experiences, the greater the risk of cognitive impairment (Elendu et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Cognitive Disorders in Post-Stroke Patients

Pratiwi,

Sari,

Mirwanti

2024

Maj. Kesehat. Indones.

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Blood circulation disorders in post-stroke patients can cause cognitive disorders. Cognitive disorders in post-stroke patients can include memory impairment, difficulty speaking, difficulty understanding things, difficulty communicating, and mood disorders. The presence of cognitive disorders in stroke patients can worsen the patient's post-stroke condition, requiring longer treatment time. This study aimed to identify the level of cognitive disorders in post-stroke patients. This research is a descriptive study to identify dementia in post-stroke patients. The sampling technique was accidental sampling for two weeks at the Neurology Polytechnic and Stroke Clinic at one of the hospitals in Bandung, totaling 83 people. The questionnaire used in this research was the Mini-Mental State Examination (MMSE). The collected data is analyzed and presented using frequency and percentage distributions. The results of this study showed that the majority of post-stroke patients (72.3%) did not experience dementia or did not experience cognitive impairment, 15.66% experienced mild cognitive impairment, and 12.04% experienced severe cognitive impairment. From the results of this study, it can be concluded that some post-stroke patients do not experience cognitive impairment, but some post-stroke patients experience severe cognitive impairment. Cognitive impairment in stroke patients can worsen the patient's condition and increase the length of treatment time. Based on the results of this research, health workers especially nurses, are expected to be able to carry out early assessments and provide particular interventions for post-stroke patients aimed at preventing the worsening of dementia and improving the patient's cognitive abilities. The intervention can be in the form of education, psychological support, and social support in the form of counseling during rehabilitation.

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“…In [31], feature vectors developed using the Doc2Vec model; then, the Random Forest RF algorithm was used for classi cation; Doc2Vec size varied from 10 to 1000 with an incremental of 10 iterations. The number of decision trees used in the forest ranged from 10 to 150, with an incremental of 10 in each iteration.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for emotion analysis in Arabic tweets

Khalil

Houby

Mohamed

2021

Preprint

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Expressing our emotions using text and emojis expressions became widespread through social media such as Facebook, Instagram, Twitter, Weibo, and LinkedIn. Nowadays, both organizations and individuals are interested in using social media to analyze people's opinions and extract sentiments and emotions. We proposed a model for multilabel emotion classification, using a bidirectional Long Short-term Memory BiLSTM deep network. It is evaluated on the Arabic tweets' dataset provided by SemEval 2018 for the E-c task. Several preprocessing steps, including ARLSTEM with some modifications, replacing emojis with corresponding text meaning from a manually built lexicon, and feature vector representation using Aravec word embedding is applied. The novelty in our research that it examines the effect of hyperparameter tuning on model performance, and it uses BiLSTM in all of its deep neural network layers. The proposed model achieves a comparable performance with state-of-the-art models using different machine learning and deep learning techniques. The system achieves about 9% enhancement in validation accuracy compared with the last best model in the same task using Support Vector classifier SVC; it outperforms the other deep neural networks (UNCCTeam) based on fully connected layers in micro F1 metric of about 4.4%.

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Amrita_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets

Cited by 4 publications

References 3 publications

Nutrition among children of migrant construction workers in Ahmedabad, India

Nutrition among children of migrant construction workers in Ahmedabad, India

Cognitive Disorders in Post-Stroke Patients

Deep Learning for emotion analysis in Arabic tweets

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