Co’FeeL: An Insightful COVID’19 Emotion Analysis based on Worldwide Twitter Data and Advanced Deep Learning Techniques (Preprint)

2020 ◽  
Author(s):  
Shruti Patil ◽  
Nikhil Matta ◽  
Dr.Ketan Kotecha
Keyword(s):  
Deep Learning ◽  
Management Strategies ◽  
Human Beings ◽  
Emotional Wellbeing ◽  
Social Fabric ◽  
The Public ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
The Government ◽  
The Masses

UNSTRUCTURED The coronavirus outbreak has altered the complete living pattern of human beings across the globe. It has disrupted the way we live, work, and play. The preventive norm of social distancing has created a void in our society’s social fabric. It has affected us not only physically or financially but has created a greater impact on our emotional wellbeing as well. This distress in our emotional quotient is a result of multiple factors such as financial implications, family member’s behavior, and support, country-specific lockdown protocols, media influence, fear of a pandemic, etc. For efficient pandemic management, there is an urgent need to understand these emotional variations among the masses, as they would provide great insights regarding the public sentiments towards the government pandemic management policies. It will also bring to light the weaker emotional sections of the masses, which we should be strengthening to face further situations more effectively. During this time, more and more people have used various social media platforms such as Twitter to stay connected and express their feelings and concerns. In this paper, we have collected and analyzed over 1 million tweets of the last five months (February-June 2020) using advanced deep learning techniques such as Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa) to study countrywide variations in emotions. We have categorized emotions into eight classes viz. anger, depression, enthusiasm, hate, relief, sadness, surprise, and worry. The outcome of this analysis, which is represented in the form of graphs, provides insights into how emotions have changed over time for various countries. These insights can be very useful not only in formulating effective pandemic management strategies but also to devise predictive strategies for the emotional wellbeing of the country as a whole and citizens in particular for future distress events.

scholarly journals COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

2022 ◽  
Vol 9 ◽  
Author(s):  
Zunera Jalil ◽  
Ahmed Abbasi ◽  
Abdul Rehman Javed ◽  
Muhammad Badruddin Khan ◽  
Mozaherul Hoque Abul Hasanat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Daily Lives ◽  
The Public ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Twitter Users

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

Water Pollution and Its Impact on Environment of Society

2016 ◽  
Vol 3 (5) ◽  
Author(s):  
N. Thyagaraju
Keyword(s):  
Water Pollution ◽  
Pollution Prevention ◽  
Human Beings ◽  
Individual Level ◽  
The Public ◽  
The Government ◽  
Living Organisms ◽  
Set Up ◽  
Biological Entities ◽  
Micro Organisms

The present seminar paper mainly highlight  the concept of  water pollution, causes of water pollution,  Its Effects, Elements of  pollutants, Methods  used to prevent the water pollution in environment  and the mandatory initiatives taken by the concerned authorities for prevention of  water pollution. Water   is essential for survival of all living organisms on the earth. Thus for human beings and plants to survive on land, water should be easily accessible. The term “Pollution” is generally refers to addition of any foreign body either living or non – living or deletion of anything that naturally exists. The basic Sources of Water pollution causes due to Culmination into lakes, rivers, ponds, seas, oceans etc. Domestic drainage and sanitary waste, Industrial drainage and sewage, Industrial waste from factories, Dumping of domestic garbage, Immersion of Idols made of plaster of Paris, Excess use of Insecticides , pesticides, fungicides, Chemical fertilizers, Soil erosion during heavy rains and floods, Natural disasters, tsunami etc. General pollutants  which are also caused for water pollution  which include Organic, Inorganic, and Biological entities, Insecticides, Pesticides, Disinfectants ,Detergents, Industrial solvents, Acids, Ammonia fertilizers, heavy metals, Harmful bacteria, Virus, Micro –Organisms and worms, Toxic chemicals. Agricultural lands become infertile and thereby production also drops, Spread of epidemic diseases like Cholera, Dysentery, Typhoid, Diarrhea, Hepatitis, Jaundice etc. The  basic responsibility of the Government, NGOs, National Pioneer scientific Research Institutions may conduct  research oriented programs on control of water pollution by create  awareness among the public through mass media and Environmental Education on recycling units,  and  water treatment plants must be established both at domestic levels and Industry levels, Every citizen must feel responsible to control water pollution. There have been many water pollution prevention acts that have been set up by the governments of the world. But these are not enough for permanent water pollution solutions. Each of us needs to take up the responsibility and do something at an everyday at individual level. Otherwise we can’t survive in a society forever in a future. 

scholarly journals Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

2021 ◽  
Author(s):  
Gaurav Chachra ◽  
Qingkai Kong ◽  
Jim Huang ◽  
Srujay Korlakunta ◽  
Jennifer Grannen ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Model ◽  
Research Community ◽  
Rescue Work ◽  
The Public ◽  
Social Media Platforms ◽  
Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

The Reforma Period in Mexico

2018 ◽  
Author(s):  
Paul Vanderwood ◽  
Robert Weis
Keyword(s):  
Catholic Church ◽  
The United States ◽  
Regional Heterogeneity ◽  
Colonial Era ◽  
Social Fabric ◽  
The Public ◽  
National Rule ◽  
The Masses ◽  
The Military ◽  
The Church

By revealing the weaknesses of its political system and the fragmentation of its social fabric, Mexico’s devastating loss to the United States in 1848 forced a reexamination of the nation’s very foundation. It also emboldened leaders to redouble efforts to either refashion Mexico into a modern, democratic republic or strengthen colonial-era institutions that had ensured unity and stability despite cultural and regional heterogeneity. Those who hoped to modernize Mexico were the liberals. Their ideas regarding the depth and pace of change varied considerably. But they coalesced around broad principles—democracy, secularism, and capitalism—that, they insisted, would help Mexico overcome the vestiges of colonialism. In pursuit of equality under the law, liberals proposed to dismantle legal privileges for nobles, ecclesiastics, and the military. In order to stimulate the economy, they wanted to force corporate entities, especially the church, to sell their lands to individual owners. Finally, liberals sought to establish the primacy of the state by granting civil leaders authority over the church. Conservatives countered that the liberal program and its exotic ideas constituted an attack on Mexico’s Hispanic Catholic legacy and would only further weaken the nation. It was a chimera, if not demagoguery, to declare the equality of citizens in a society where the masses were illiterate, isolated hamlets who barely spoke Spanish, and residents in the far-flung regions regarded national rule with deep suspicion. Conservatives feared that the liberal program would foster more of the peasant revolts, threats of regional succession, and racial antagonism that had roiled the nation since independence. They wanted to conserve the pillars of order—the military and the Catholic Church—reinstate monarchism, and curtail political participation. Liberals and conservatives vociferously debated these divergent visions in the public forum. But ultimately their differences plunged the country into civil war.

How to strengthen the social media interactivity of e-government

2016 ◽  
Vol 40 (1) ◽  
pp. 79-96 ◽  
Author(s):  
xiaoling Hao ◽  
Daqing Zheng ◽  
Qingfeng Zeng ◽  
Weiguo Fan
Keyword(s):  
Social Media ◽  
Local Governments ◽  
Structural Features ◽  
Content Type ◽  
The Public ◽  
Positive Effects ◽  
Popular Government ◽  
The Social ◽  
Social Media Platforms ◽  
The Government

Purpose – The purpose of this paper is to explore how to use social media in e-government to strengthen interactivity between government and the general public. Design/methodology/approach – Categorizing the determinants to interactivity covering depth and breadth into two aspects that are the structural features and the content features, this study employs general linear model and ANOVA method to analyse 14,910 posts belonged to the top list of the 96 most popular government accounts of Sina, one of the largest social media platforms in China. Findings – The main findings of the research are that both variables of the ratio of multimedia elements, and the ratio of external links have positive effects on the breadth of interactivity, while the ratio of multimedia features, and the ratio of originality have significant effects on the depth of interactivity. Originality/value – The contributions are as follows. First, the authors analyse the properties and the topics of government posts to draw a rich picture of how local governments use the micro-blog as a communications channel to interact with the public. Second, the authors conceptualize the government online interactivity in terms of the breadth and depth. Third, the authors identify factors that will enhance the interactivity from two aspects: structural features and content features. Lastly, the authors offer suggestions to local governments on how to strengthen the e-government interactivity in social media.

scholarly journals Online Public Shaming Approach using Deep Learning Techniques

2021 ◽  
Vol 23 (3) ◽  
Author(s):  
Mehdi Surani ◽  
◽  
Ramchandra Mangrulkar ◽  
Keyword(s):  
Deep Learning ◽  
Hate Speech ◽  
Severe Depression ◽  
Learning System ◽  
Online Bullying ◽  
Digital World ◽  
Learning Techniques ◽  
Proposed Model ◽  
Social Media Platforms ◽  
The Impact

Public shaming on social media platforms like Twitter / Instagram / Facebook etc. have recently increased from the past years. This results in affecting an individual’s social, political, mental and financial life. The impact can range from mild bullying to severe depression. With the growing leniency on these social platforms, many people have started misusing the opportunity by turning to online bullying and hate speech. When something is posted online, it stays there forever and it becomes extremely hard taking something out of the digital world. Manually locating and categorizing such comments is a lengthy procedure and just cannot be relied upon. To solve this challenge, automation was performed to identify and classify the shamers. This has been done using the classic SVM model which worked on a given quantity of data. To identify the negative content being posted and discussed online, this paper further explores the deep learning system which can successfully classify these content pieces into proper labels. The text-based Convolution Neural Network (CNN) is the proposed model in this paper for this analysis.

scholarly journals Deep learning models for classification of gases detected by sensor arrays of artificial nose

2019 ◽  
Author(s):  
Ismael Araujo ◽  
Juan Gamboa ◽  
Adenilton Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Human Beings ◽  
Learning Models ◽  
Classification Problems ◽  
Artificial Nose ◽  
Learning Techniques

To recognize patterns that are usually imperceptible by human beings has been one of the main advantages of using machine learning algorithms The use of Deep Learning techniques has been promising to the classification problems, especially the ones related to image classification. The classification of gases detected by an artificial nose is one other area where Deep Learning techniques can be used to seek classification improvements. Succeeding in a classification task can result in many advantages to quality control, as well as to preventing accidents. In this work, it is presented some Deep Learning models specifically created to the task of gas classification.

scholarly journals FILANTROPI BERBASIS FAITH BASED ORGANIZATION DI INDONESIA (Studi Kasus Program PKPU)

2018 ◽  
Vol 2 (1) ◽  
pp. 13-24
Author(s):  
Amril Maryolo
Keyword(s):  
Sense Of Community ◽  
Social Inequalities ◽  
Public Welfare ◽  
Strong Sense ◽  
Human Beings ◽  
The Public ◽  
Faith Based ◽  
Humanitarian Organizations ◽  
The Social ◽  
The Government

Philanthropy is an act of generosity that has a sense of sympathy for human beings. Generosity is an integral part of the character of Indonesian society, derived from religious wisdom, culture, and a strong sense of community. The existence of Faith Based Organization (religious-based organization) helps the government in overcoming the social inequalities that occur in middle and lower society. One of the humanitarian organizations based on Islam in Indonesia is the Post of Justice Peduli Ummat (PKPU) which provides assistance, various forms of social activities in various fields. The presence of these humanitarian agencies in Indonesia marks the "new practice of philanthropy" of the Islamic philanthropy movement in realizing the public welfare.

scholarly journals Public Opinions and Concerns Regarding the Canadian Prime Minister’s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques

10.2196/23957 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e23957
Author(s):  
Chengda Zheng ◽  
Jia Xue ◽  
Yumin Sun ◽  
Tingshao Zhu
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Public Discourse ◽  
Latent Dirichlet Allocation ◽  
Prime Minister ◽  
Machine Learning Techniques ◽  
Future Health ◽  
The Public ◽  
Learning Techniques ◽  
The Government

Background During the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government’s responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel. Objective The aim of this study was to examine comments on Canadian Prime Minister Trudeau’s COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time. Methods We used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau’s COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes. Results We found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau’s policies, essential work and frontline workers, individuals’ financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China’s relationship, vaccines, and reopening. Conclusions This study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau’s daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies.

scholarly journals Comparative Analysis of Deep Learning Techniques to detect Online Public Shaming

2021 ◽  
Vol 40 ◽  
pp. 03030
Author(s):  
Mehdi Surani ◽  
Ramchandra Mangrulkar
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Freedom Of Speech ◽  
Learning Approaches ◽  
Learning Models ◽  
Media Usage ◽  
The Past ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Machine Learning Models

Over the past years the exponential growth of social media usage has given the power to every individual to share their opinions freely. This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent of cyberbullying. However, determining abusive content is not a difficult task and many social media platforms have solutions available already but at the same time, many are searching for more efficient ways and solutions to overcome this issue. Traditional models explore machine learning models to identify negative content posted on social media. Shaming categories are explored, and content is put in place according to the label. Such categorization is easy to detect as the contextual language used is direct. However, the use of irony to mock or convey contempt is also a part of public shaming and must be considered while categorizing the shaming labels. In this research paper, various shaming types, namely toxic, severe toxic, obscene, threat, insult, identity hate, and sarcasm are predicted using deep learning approaches like CNN and LSTM. These models have been studied along with traditional models to determine which model gives the most accurate results.

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