scholarly journals National Leaders’ Usage of Twitter in Response to COVID-19: A Sentiment Analysis

2021 ◽  
Vol 6 ◽  
Author(s):  
Yuming Wang ◽  
Stephen M Croucher ◽  
Erika Pearson
Keyword(s):  
Public Health ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Prime Minister ◽  
Predictive Modelling ◽  
Intelligence Sharing ◽  
Present Information ◽  
National Leaders ◽  
Public Health Information ◽  
Research Questions

Twitter is a powerful tool for world leaders to disseminate public health information and to reach citizens. While Twitter, like other platforms, affords world leaders the opportunity to rapidly present information to citizens, the discourse is often politically framed. In this study, we analysed how leaders’ of the Five Eyes intelligence sharing group use Twitter to frame the COVID-19 virus. Specifically, four research questions were explored: 1) How frequently did each leader tweet about COVID-19 in 2020? 2) Which frames emerged from tweet content of each leader regarding COVID-19? 3) What was the overall tweet valence of each leader regarding COVID-19? and 4) To what extent can leaders’ future tweets be predicted by the data? We used natural language processing (NLP) and conducted sentiment analysis via Python to identify frames and to compare the leaders’ messaging. Results showed that of the leaders, President Trump tweeted the most, with Prime Minister Morrison posting the least number of tweets. The majority of each leaders’ tweets were positive, while President Trump had the most negative tweets. Predictive modelling of tweet behavior was highly accurate.

Psychological Foundations of the Cleanliness Initiative in Ahmedabad City of India

2020 ◽  
pp. 23-27
Author(s):  
Ramadhar Singh ◽  
Neeraj Pandey
Keyword(s):  
Prime Minister ◽  
Active Participation ◽  
Civil Servants ◽  
New Delhi ◽  
National Leaders ◽  
The City

Spitting on the roads of and littering around a city in India have been of concern to national leaders and civil servants since the pre-independence years. It was unsurprising, therefore, that the Prime Minister Narendra Modi launched the Swachh Bharat Abhiyan (SBA) as a nation-wide cleanliness campaign on October 2, 2014 at Rajghat, New Delhi. The cleanliness initiative by the Ahmedabad Municipal Commissioner (i) dissuades spitting on the roads and littering around the city, (ii) collects fines from those whose photos are captured by CCTV cameras, and (iii) invites active participation of all residents of Ahmedabad in the cleanliness drive. The authors present psychological foundations of this initiative, arguing that all residents ought to hold the offender and anyone else associated with such an offense as accountable. Further, they raise four new issues with the current cleanliness drive and offer suggestions for how to resolve them.

Japanese-language videos on YouTube as a source of public health information for COVID-19 (Preprint)

2020 ◽  
Author(s):  
Takeo Yasu
Keyword(s):  
Public Health ◽  
Health Information ◽  
Scientific Evidence ◽  
The State ◽  
Video Content ◽  
Sources Of Information ◽  
State Of Emergency ◽  
Public Health Information ◽  
Youtube Videos ◽  
Spread Of Infection

BACKGROUND Serious public health problems, such as the COVID-19 pandemic, can cause an infodemic. Sources of information that may cause an infodemic include social networking services; YouTube, which consists of content created and uploaded by individuals, is one such source. OBJECTIVE To survey the content and changes in YouTube videos that present public health information about COVID-19 in Japan. METHODS We surveyed YouTube content regarding public health information pertaining to COVID-19 in Japan. YouTube searches were performed on March 6, 2020 (before the state of emergency), April 14 (during the state of emergency), and May 27 (after the state of emergency was lifted), with 136, 113, and 140 sample videos evaluated, respectively. The main outcome measures were: (1) The total number of views for each video, (2) video content, and (3) the usefulness of the video. RESULTS In the 100 most viewed YouTube videos during the three periods, the number of videos on public health information in March was significantly higher than in May (p = .02). Of the 331 unique videos, 9.1% (n = 30) were released by healthcare professionals. Useful videos providing public health information about the prevention of the spread of infection comprised only 13.0% of the sample but were viewed significantly more often than not useful videos (p = .006). CONCLUSIONS Individuals need to take care when obtaining information from YouTube before or early in a pandemic, during which time scientific evidence is scarce.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

Assessment of Agreement Between Human Ratings and Lexicon-Based Sentiment Ratings of Open-Ended Responses on a Behavioral Rating Scale

Assessment ◽  
2021 ◽  
pp. 107319112199646
Author(s):  
Olivia Gratz ◽  
Duncan Vos ◽  
Megan Burke ◽  
Neelkamal Soares
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Rating Scales ◽  
Clinical Decision Making ◽  
Rating Scale ◽  
Clinical Decision ◽  
Assessment System ◽  
Word Count ◽  
Positive Correlation ◽  
Sentiment Score

To date, there is a paucity of research conducting natural language processing (NLP) on the open-ended responses of behavior rating scales. Using three NLP lexicons for sentiment analysis of the open-ended responses of the Behavior Assessment System for Children-Third Edition, the researchers discovered a moderately positive correlation between the human composite rating and the sentiment score using each of the lexicons for strengths comments and a slightly positive correlation for the concerns comments made by guardians and teachers. In addition, the researchers found that as the word count increased for open-ended responses regarding the child’s strengths, there was a greater positive sentiment rating. Conversely, as word count increased for open-ended responses regarding child concerns, the human raters scored comments more negatively. The authors offer a proof-of-concept to use NLP-based sentiment analysis of open-ended comments to complement other data for clinical decision making.

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

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