Nepal government is secure for now

Significance Assembly members are elected indirectly. Last July, Sher Bahadur Deuba was appointed prime minister in line with a Supreme Court order. Deuba’s multi-party coalition has a sizeable majority in the House of Representatives, parliament’s lower house, and a comfortable one in the National Assembly. Impacts If local elections take place in May as currently planned, they will serve as a barometer of public sentiment towards the leading parties. KP Sharma Oli, whom Deuba replaced as premier, will be eyeing a return to power. Politicking risks distracting the government from pursuing important policy goals.

The relationship between sentiment score and COVID-19 cases in the United States

The coronavirus disease (COVID-19) continues to have devastating effects across the globe. No nation has been free from the uncertainty brought by this pandemic. The health, social and economic tolls associated with it are causing strong emotions and spreading fear in people of all ages, genders and races. Since the beginning of the COVID-19 pandemic, many have expressed their feelings and opinions related to a wide range of aspects of their lives via Twitter. In this study, we consider a framework for extracting sentiment scores and opinions from COVID-19–related tweets. We connect users’ sentiment with COVID-19 cases across the United States and investigate the effect of specific COVID-19 milestones on public sentiment. The results of this work may help with the development of pandemic-related legislation, serve as a guide for scientific work, as well as inform and educate the public on core issues related to the pandemic.

Tracking sentiments toward fat acceptance over a decade on Twitter

The fat acceptance (FA) movement aims to counteract weight stigma and discrimination against individuals who are overweight/obese. We developed a supervised neural network model to classify sentiment toward the FA movement in tweets and identify links between FA sentiment and various Twitter user characteristics. We collected any tweet containing either “fat acceptance” or “#fatacceptance” from 2010–2019 and obtained 48,974 unique tweets. We independently labeled 2000 of them and implemented/trained an Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) neural network that incorporates transfer learning from language modeling to automatically identify each tweet’s stance toward the FA movement. Our model achieved nearly 80% average precision and recall in classifying “supporting” and “opposing” tweets. Applying this model to the complete dataset, we observed that the majority of tweets at the beginning of the last decade supported FA, but sentiment trended downward until 2016, when support was at its lowest. Overall, public sentiment is negative across Twitter. Users who tweet more about FA or use FA-related hashtags are more supportive than general users. Our findings reveal both challenges to and strengths of the modern FA movement, with implications for those who wish to reduce societal weight stigma.

Chennai Floods 2021: Sentiment Analysis of Twitter Data using Tweepy and TextBlob

The rise in the usage of Twitter for the exclamation of the problems worldwide and also as a ‘review system,’ where the customers can directly hold an entity responsible in front of the public by tweeting and tagging them, gives them immense power and counts towards being an advantage for researchers to analyze such data that can be scraped and used through APIs for a variety of purposes. Through this research, our motive is to analyze the 2021 Chennai floods with data sourced from twitter to understand the public sentiment during the 14-day span. The same is achieved with the help of Tweepy to authenticate data extraction from Twitter and TextBlob, for the classification of sentiment tags - positive, negative, and neutral. The result of this study focuses on the visualization of our findings, with various charts and metrics indicating the sentiment of the tweets we have scraped and analyzed. Keywords: Sentiment Analysis, WordCloud, Subjectivity, Polarity, Chennai Floods

Sewol Ferry Disaster

This chapter provides background information regarding South Koreans’ anger and frustration with the Park Geun-hye administration, which led to a series of candlelight vigils calling for her impeachment. In particular, it analyzes public sentiment surrounding President Park’s handling of the 2014 sinking of the Sewol ferry in which 250 South Korean high school students died. Prior to the revelation of the Park-Choi corruption scandal, the Sewol ferry disaster, caused by human error and poorly managed by the Park government, was the most significant event that contributed to reaching a tipping point for the impeachment movement. The Park-Choi scandal served as a trigger for public outrage, which had been simmering for several years. This chapter analyzes how outrage and embarrassment spread in the information ecosystem at that time and served to motivate people to participate in the impeachment vigils.

Sentiment Analysis on the Ratification of Penghapusan Kekerasan Seksual Bill on Twitter

Rancangan Undang-Undang Penghapusan Kekerasan Seksual (RUU PKS), or the bill of the Republic of Indonesia on the Elimination of Sexual Violence, is a bill that discusses sexual violence, victim protection and its scope concern the matters related to sexual violence. Elimination of sexual violence according to the PKS Bill aims to prevent all forms of violence. These discussions and conversations also occur on social media, especially on Twitter. Taking public sentiment is significant in choosing the proper messages, interference, and policy. Sentiment analysis is a field of study that analyses opinions, sentiments, judgments, evaluations of a person attitudes and emotions regarding a particular topic, service, product, individual, organization or activity. This study used the method of crawling to get data from Twitter. Then data cleansing, data processing is carried out using Bernoulli, Logistic Regression, and Support Vector Classification (SVC) algorithm. The data is then evaluated using three methods: accuracy, classification report, and confusion matrix. Based on the three algorithms used, it is found that all methods are equally accurate with 0.65. This study found positive, negative, and neutral sentiments expressed to the bill of Elimination of Sexual Violence through comments. It is shown that most people using the keyword “RUU PKS” are positive to the bill of Elimination of Sexual Violence (RUU PKS) while most people’s sentiments using #RUUPKSBukanSolusi are negative to the bill.

Failure of the semi-authoritarian system: Losing ways of absorbing public voices into the post-handover Hong Kong government

In the past decade, Hong Kong has undergone various large-scale protests, such as the 2014 Occupy Central and the 2019 Anti-Extradition Protests. One of the reasons for such popular grievance was that the government could not grasp the change in public sentiment and opinion. Before the handover, although the governor held the centralized power, the colonial authorities still had ways to collect public opinions to avoid departing from the citizens’ views. The model was called the ‘administrative absorption of politics’. The Chinese authorities attempted to preserve the original advisory system to depoliticize the policy-making process after the handover. This article contributes to the understanding of the development of the cooptation system in Hong Kong and its failure in the 2010s based on the insights of legislators. It also highlights the importance of participation and salient control in the cooptation system to balance public views in a semi-authoritarian society.

Public sentiment of the population in the new social reality: a municipal cross-section

The article analyses the dynamics of public sentiment of residents of the Vologda Region. The author’s methodology for index analysis of public sentiment of the region, using the results of sociological monitoring, has been presented. At the same time, the main emphasis has been made on assessing the peculiarities of public sentiment of residents of municipalities – the cities of Vologda and Cherepovets in the context of changing social reality. The results of the study showed that the analysis of public sentiment in the monitoring regime makes it possible to obtain important information about the quality of state and municipal administration and determine the areas for improving their effectiveness, which is particularly relevant in the context of the socio-economic and epidemiological crisis.

Immigrant Essential Workers During the COVID-19 Pandemic

The COVID-19 pandemic affected everyone in the United States, and essential workers across industries like health care, agriculture, retail, transportation and food supply were key to our survival. Immigrants, overrepresented in essential industries but largely invisible in the public eye, were critical to our ability to weather the pandemic and recover from it. But who are they? How did they do the riskiest of jobs in the riskiest of times? And how were both U.S.-born and foreign-born residents affected? This report explores the crucial contributions of immigrant essential workers, their impact on the lives of those around them, and how they were affected by the pandemic, public sentiment and policies. It further explores the contradiction of immigrants being essential to all of our well-being yet denied benefits, protections and rights given to most others. The pandemic revealed the significant value of immigrant essential workers to the health of all Americans. This report places renewed emphasis on their importance to national well-being. The report first provides a demographic picture of foreign-born workers in key industries during the pandemic using U.S. Census Bureau American Community Survey (ACS) data. Part I then gives a detailed narrative of immigrants’ experiences and contributions to the country’s perseverance during the pandemic based on interviews with immigrant essential workers in California, Minnesota and Texas, as well as with policy experts and community organizers from across the country. Interviewees include: ■ A food packing worker from Mexico who saw posters thanking doctors and grocery workers but not those like her working in the fields. ■ A retail worker from Argentina who refused the vaccine due to mistrust of the government. ■ A worker in a check cashing store from Eritrea who felt a “responsibility to be able to take care of people” lining up to pay their bills. Part II examines how federal and state policies, as well as increased public recognition of the value of essential workers, failed to address the needs and concerns of immigrants and their families. Both foreign-born and U.S.-born people felt the consequences. Policies kept foreign-trained health care workers out of hospitals when intensive care units were full. They created food and household supply shortages resulting in empty grocery shelves. They denied workplace protections to those doing the riskiest jobs during a crisis. While legislation and programs made some COVID-19 relief money available, much of it failed to reach the immigrant essential workers most in need. Part II also offers several examples of local and state initiatives that stepped in to remedy this. By looking more deeply at the crucial role of immigrant essential workers and the policies that affect them, this report offers insight into how the nation can better respond to the next public health crisis.