Use of Sentiment Analysis in Order to Learn the Public Perception for Organizations Providing Covid-19 Vaccinations in the UAE

2021 ◽  
Author(s):  
Abdulrahman Radaideh ◽  
Fekri Dweiri ◽  
Mohammad Obaidat
Keyword(s):  
Sentiment Analysis ◽  
Public Perception ◽  
The Public

scholarly journals Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets

2020 ◽  
Vol 17 (10) ◽  
pp. 3464 ◽  
Author(s):  
Viju Raghupathi ◽  
Jie Ren ◽  
Wullianallur Raghupathi
Keyword(s):  
United States ◽  
Sentiment Analysis ◽  
Public Perception ◽  
Ebola Virus ◽  
Herd Immunity ◽  
The United States ◽  
The Public ◽  
Time Period ◽  
Document Frequency ◽  
Measles Outbreak

Text analysis has been used by scholars to research attitudes toward vaccination and is particularly timely due to the rise of medical misinformation via social media. This study uses a sample of 9581 vaccine-related tweets in the period 1 January 2019 to 5 April 2019. The time period is of the essence because during this time, a measles outbreak was prevalent throughout the United States and a public debate was raging. Sentiment analysis is applied to the sample, clustering the data into topics using the term frequency–inverse document frequency (TF-IDF) technique. The analyses suggest that most (about 77%) of the tweets focused on the search for new/better vaccines for diseases such as the Ebola virus, human papillomavirus (HPV), and the flu. Of the remainder, about half concerned the recent measles outbreak in the United States, and about half were part of ongoing debates between supporters and opponents of vaccination against measles in particular. While these numbers currently suggest a relatively small role for vaccine misinformation, the concept of herd immunity puts that role in context. Nevertheless, going forward, health experts should consider the potential for the increasing spread of falsehoods that may get firmly entrenched in the public mind.

scholarly journals A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data

2021 ◽  
Vol 11 (4) ◽  
pp. 1894
Author(s):  
Yong Sun ◽  
Fengxiang Jin ◽  
Yan Zheng ◽  
Min Ji ◽  
Huimeng Wang
Keyword(s):  
Air Pollution ◽  
Sentiment Analysis ◽  
Public Perception ◽  
Shandong Province ◽  
Kriging Interpolation ◽  
Social Media Data ◽  
The Public ◽  
Coastal Cities ◽  
Media Data ◽  
Monitoring Stations

Severe air pollution problems have led to a rise in the Chinese public’s concern, and it is necessary to use monitoring stations to monitor and evaluate pollutant levels. However, monitoring stations are limited, and the public is everywhere. It is also essential to understand the public’s awareness and behavioral response to air pollution. Air pollution complaint data can more directly reflect the public’s real air quality perception than social media data. Therefore, based on air pollution complaint data and sentiment analysis, we proposed a new air pollution perception index (APPI) in this paper. Firstly, we constructed the emotional dictionary for air pollution and used sentiment analysis to calculate public complaints’ emotional intensity. Secondly, we used the piecewise function to obtain the APPI based on the complaint Kernel density and complaint emotion Kriging interpolation, and we further analyzed the change of center of gravity of the APPI. Finally, we used the proposed APPI to examine the 2012 to 2017 air pollution complaint data in Shandong Province, China. The results were verified by the POI (points of interest) data and word cloud analysis. The results show that: (1) the statistical analysis and spatial distribution of air pollution complaint density and public complaint emotion intensity are not entirely consistent. The proposed APPI can more reasonably evaluate the public perception of air pollution. (2) The public perception of air pollution tends to the southwest of Shandong Province, while coastal cities are relatively weak. (3) The content of public complaints about air pollution mainly focuses on the exhaust emissions of enterprises. Moreover, the more enterprises gather in inland cities, the public perception of air pollution is stronger.

scholarly journals Public Responses to Air Pollution in Shandong Province Using the Online Complaint Data

2021 ◽  
Vol 10 (3) ◽  
pp. 126
Author(s):  
Yong Sun ◽  
Min Ji ◽  
Fengxiang Jin ◽  
Huimeng Wang
Keyword(s):  
Air Pollution ◽  
Sentiment Analysis ◽  
Public Perception ◽  
Analytical Framework ◽  
Shandong Province ◽  
Emotional Intensity ◽  
Air Pollution Control ◽  
Public Response ◽  
The Public ◽  
Exhaust Gas Emissions

As air users, the public is also participants in air pollution control and important evaluators of environmental protection. Therefore, understanding the public perception and response to air pollution is an essential part of improving air governance. This study proposed an analytical framework for public response to air pollution based on online complaint data and sentiment analysis. In the proposed framework, the emotional dictionary of air pollution was firstly constructed using microblog data and complaint data. Secondly, the emotional dictionary of air pollution and the sentiment analysis method were used to calculate public complaints’ emotional intensity. Besides, the spatial and temporal characteristics of air pollution complaint data and public emotional intensity, the complaints content, and their correlation with PM2.5 (particulate matters smaller than 2.5 micrometers) and PM10 were analyzed using address matching, spatial analysis, and word cloud analysis. Finally, the proposed framework was applied to 13,469 air pollution complaint data in Shandong Province from 2012 to 2018. The obtained results indicated that: the public was mainly complaining about the exhaust gas emissions from enterprises and factories. Spatially, the geographical center of complaint data was located in the inland industrial urban agglomeration of Shandong Province. Correlatively, air pollution complaints’ negative emotional intensity was significantly negatively correlated with PM2.5 (−0.73). Moreover, the number of public complaints about air pollution and the intensity of negative emotions also decreased with improved air quality in Shandong Province in recent years.

scholarly journals COVID-19 and Black Fungus: Analysis of the Public Perception through Machine Learning

2021 ◽  
Author(s):  
Muhammad Nazrul Islam ◽  
Nafiz Imtiaz Khan ◽  
Tahasin Mahmud
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Public Perception ◽  
Public Perceptions ◽  
Support Vector ◽  
Machine Model ◽  
The Public ◽  
Class Labels ◽  
Media Data

While COVID-19 is ravaging the lives of millions of people across the globe, a second pandemic 'black fungus' has surfaced robbing people of their lives especially people who are recovering from coronavirus. Again, the public perceptions regarding such pandemics can be investigated through sentiment analysis of social media data. Thus the objective of this study is to analyze public perceptions through sentiment analysis regarding black fungus during the time of the COVID-19 pandemic. To attain the objective, first, a Support Vector Machine model, with an average AUC of 82.75\%, was developed to classify user sentiments in terms of anger, fear, joy, and sad. Next, this Support Vector Machine is used to supervise the class labels of the public tweets (n = 6477) related to COVID-19 and black fungus. As outcome, this study found that public perceptions belong to sad (n = 2370, 36.59 \%), followed by joy ( n = 2095, 32.34\%), fear ( n = 1914, 29.55 \%) and anger ( n = 98, 1.51\%) towards black fungus during COVID-19 pandemic. This study also investigated public perceptions of some critical concerns (e.g., education, lockdown, hospital, oxygen, quarantine, and vaccine) and it was found that public perceptions of these issues varied. For example, for the most part, people exhibited fear in social media about education, hospital, vaccine while some people expressed joy about education, hospital, vaccine, and oxygen.

Method to Rank Academic Institutes by the Sentiment Analysis of Their Online Reviews

2020 ◽  
pp. 1-26
Author(s):  
Simran Sidhu ◽  
Surinder Singh Khurana
Keyword(s):  
Sentiment Analysis ◽  
Public Perception ◽  
Online Reviews ◽  
Online Review ◽  
University Rankings ◽  
Ranking List ◽  
The Public ◽  
Social Media Platforms ◽  
Good Potential ◽  
Sentiment Score

A large number of reviews are expressed on academic institutes using the online review portals and other social media platforms. Such reviews are a good potential source for evaluating the Indian academic institutes. This chapter aimed to collect and analyze the sentiments of the online reviews of the academic institutes and ranked the institutes on the basis of their garnered online reviews. Lexical-based sentiment analysis of their online reviews is used to rank academic institutes. Then these rankings were compared with the NIRF PR Overall University Rankings List 2017. The outcome of this work can efficiently support the overall university rankings of the NIRF ranking list to enhance NIRF's public perception parameter (PRPUB). The results showed that Panjab University achieved the highest sentiment score, which was followed by BITS-Pilani. The results highlighted that there is a significant gap between NIRF's perception rankings and the perception of the public in general regarding an academic institute as expressed in online reviews.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

Public Administration at the Movies

Public Voices ◽  
2017 ◽  
Vol 4 (2) ◽  
pp. 3 ◽  
Author(s):  
Lyn Holley ◽  
Rebecca K Lutte
Keyword(s):  
Public Administration ◽  
Public Perception ◽  
Public Information ◽  
Public Employees ◽  
Paper Briefly ◽  
Negative Image ◽  
The Public ◽  
Popular Films ◽  
Positive Message ◽  
American Society

This paper briefly summarizes evidence for the influence of popular films on public perception of government and on public policy.  Two films examined through the lens of public administration, and the lessons they teach about public administration, are exposed.  One film, Ghostbusters conveys a strongly negative image, and the other, A Thousand Heroes a strongly positive message.  Only Ghostbusters was and remains popular and profitable.  Public information efforts by government and the public administration community have been limited or reactive.  The authors argue for the increased support for public information initiatives such as those of the Public Employees Roundtable (PER) and  the American Society of Public Administration (ASPA).

scholarly journals ‘Ruined’ lives: Mediated white male victimhood

2021 ◽  
Vol 24 (1) ◽  
pp. 60-80
Author(s):  
Sarah Banet-Weiser
Keyword(s):  
Social Media ◽  
Sexual Harassment ◽  
Sexual Violence ◽  
Public Perception ◽  
White Male ◽  
The Public ◽  
Multi Media ◽  
Patriarchal Power ◽  
Almost All ◽  
False Accusations

When the hashtag #metoo began to circulate in digital and social media, it challenged a familiar interpretation of those who are raped or sexually harassed as victims, positioning women as embodied agents. Yet, almost exactly a year after the #metoo movement shot to visible prominence, a different, though eerily similar, story began to circulate on the same multi-media platforms as #metoo: a story about white male victimhood. Powerful men in positions of privilege (almost always white) began to take up the mantle of victimhood as their own, often claiming to be victims of false accusations of sexual harassment and assault by women. Through the analysis of five public statements by highly visible, powerful men who have been accused of sexual violence, I argue that the discourse of victimhood is appropriated not by those who have historically suffered but by those in positions of patriarchal power. Almost all of the statements contain some sentiment about how the accusation (occasionally acknowledging the actual violence) ‘ruined their life’, and all of the statements analyzed here center the author, the accused white man, as the key subject in peril and the authors position themselves as truth-tellers about the incidents. These statements underscore certain shifts in the public perception of sexual violence; the very success of the #metoo movement in shifting the narrative has meant that men have had to defend themselves more explicitly in public. In order to wrestle back a hegemonic gender stability, these men take on the mantle of victimhood themselves.

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