Tracking Public Attitudes toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-based Sentiment Analysis (Preprint)

2021 ◽  
Author(s):  
Hyeju Jang ◽  
Emily Rempel ◽  
Ian Roe ◽  
Giuseppe Carenini ◽  
Naveed Zafar Janjua
Keyword(s):  
Public Health ◽  
Sentiment Analysis ◽  
Vaccine Hesitancy ◽  
Public Health Response ◽  
Major Barrier ◽  
Positive Sentiment ◽  
Twitter Users ◽  
Key Aspects ◽  
Negative Sentiment ◽  
Health Experts

BACKGROUND The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation poses a major barrier to achieving herd immunity. OBJECTIVE We aim to investigate Twitter users’ attitudes toward COVID-19 vaccination in Canada after vaccine rollout. METHODS We applied a weakly-supervised aspect-based sentiment analysis (ABSA) technique on COVID-19 vaccination-related tweets in Canada. Automatically-generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiment toward “vaccination” changed over time. In addition, we analyzed the most retweeted/liked tweets by observing most frequent nouns and sentiments toward key aspects. RESULTS After training tweets using an ABSA system, we obtained 108 aspect terms (e.g., “immunity” and “pfizer”) and 6,793 opinion terms (e.g., “trustworthy” for the positive sentiment and “jeopardize” for the negative sentiment). While manually verifying/editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for more analysis. The results showed that the top-ranked automatically-extracted aspects include “risk”, “delay”, and “hope”. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to “vaccine distribution”, “side effects”, “allergy”, “reactions” and “anti-vaxxer”, and positive sentiments related to “vaccine campaign”, “vaccine candidates”, and “immune response”. All these results indicate that the Twitter users express concerns about the safety of vaccines, but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of all the tweets, the most retweeted/liked tweets showed more positive sentiment overall, especially about vaccination itself. When looking more closely, the most retweeted/liked tweets showed an interesting dichotomy in Twitter users, i.e., the “anti-vaxxer” population who used a negative sentiment as a means to discourage vaccination and the “Covid Zero” population who used negative sentiments to encourage vaccinations while critiquing the public health response. CONCLUSIONS This study is the first to examine public sentiments toward COVID-19 vaccination on tweets over an extended period of time in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination, and get closer to the goal of ending the pandemic.

scholarly journals Analysis of Social Media Users Sentiments against Omnibus Law Based on Hashtags on Twitter

SISTEMASI ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 197
Author(s):  
Okta Fanny ◽  
Heri Suroyo
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Main Topic ◽  
Accuracy Score ◽  
Test Results ◽  
Bayes Classifier ◽  
The Public ◽  
Average Accuracy ◽  
Positive Sentiment ◽  
Negative Sentiment

From the research that has been done, it can be concluded that Sentiment Analysis can be used to know the sentiment of the public, especially Twitter netizens against omnibus law. After the sentiment analysis, it looks neutral artmen with the largest percentage of 55%, then positive sentiment by 35% and negative sentiment by 10%. The results of the analysis showed that the Naïve Bayes Classifier method provides classification test results with accuracy in Hashtag Pro with an average accuracy score of 92.1%, precision values with an average of 94.8% and recall values with an average of 90.7%. While Hashtag Counter For data classification, with an average accuracy value of 98.3%, precision value with an average of 97.6% and recall value with an average of 98.7%. The result of text cloud analysis conducted on a combination of hashtags both Hashtag pros and Hashtags cons, the dominant word appears is Omnibus Law which means that all hashtags in scrap is really discussing the main topic that is about Omnibus Law

scholarly journals Mining social media data to investigate patient perceptions regarding DMARD pharmacotherapy for rheumatoid arthritis

2020 ◽  
Vol 79 (11) ◽  
pp. 1432-1437 ◽  
Author(s):  
Chanakya Sharma ◽  
Samuel Whittle ◽  
Pari Delir Haghighi ◽  
Frada Burstein ◽  
Roee Sa'adon ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Social Media ◽  
Side Effects ◽  
Sentiment Analysis ◽  
Computer Algorithms ◽  
Social Media Platforms ◽  
Synthetic Agents ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
Media Data

ObjectivesWe hypothesise that patients have a positive sentiment regarding biological/targeted synthetic disease modifying anti-rheumatic drugs (b/tsDMARDs) and a negative sentiment towards conventional synthetic agents (csDMARDs). We analysed discussions on social media platforms regarding DMARDs to understand the collective sentiment expressed towards these medications.MethodsTreato analytics were used to download all available posts on social media about DMARDs in the context of rheumatoid arthritis. Strict filters ensured that user generated content was downloaded. The sentiment (positive or negative) expressed in these posts was analysed for each DMARD using sentiment analysis. We also analysed the reason(s) for this sentiment for each DMARD, looking specifically at efficacy and side effects.ResultsComputer algorithms analysed millions of social media posts and included 54 742 posts about DMARDs. We found that both classes had an overall positive sentiment. The ratio of positive to negative posts was higher for b/tsDMARDs (1.210) than for csDMARDs (1.048). Efficacy was the most commonly mentioned reason in posts with a positive sentiment and lack of efficacy was the most commonly mentioned reason for a negative sentiment. These were followed by the presence/absence of side effects in negative or positive posts, respectively.ConclusionsPublic opinion on social media is generally positive about DMARDs. Lack of efficacy followed by side effects were the most common themes in posts with a negative sentiment. There are clear reasons why a DMARD generates a positive or negative sentiment, as the sentiment analysis technology becomes more refined, targeted studies could be done to analyse these reasons and allow clinicians to tailor DMARDs to match patient needs.

scholarly journals In the mood: the dynamics of collective sentiments on Twitter

2016 ◽  
Vol 3 (6) ◽  
pp. 160162 ◽  
Author(s):  
Nathaniel Charlton ◽  
Colin Singleton ◽  
Danica Vukadinović Greetham
Keyword(s):  
Large Dataset ◽  
Agent Based ◽  
Positive Sentiment ◽  
External Events ◽  
Twitter Users ◽  
Negative Sentiment ◽  
Scoring Algorithms ◽  
The Relationship ◽  
Structurally Stable ◽  
Average User

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source S enti S trength program. Specifically we make three contributions. Firstly, we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example, they use positive sentiment more often and negative sentiment less often. Secondly, we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable with those obtained from our empirical dataset.

scholarly journals Twitter analysis of the orthodontic patient experience with braces vs Invisalign

2016 ◽  
Vol 87 (3) ◽  
pp. 377-383 ◽  
Author(s):  
Daniel Noll ◽  
Brendan Mahon ◽  
Bhavna Shroff ◽  
Caroline Carrico ◽  
Steven J. Lindauer
Keyword(s):  
Sentiment Analysis ◽  
Patient Experience ◽  
Large Scale ◽  
Specific Content ◽  
Significant Difference ◽  
Data Collection Program ◽  
Twitter Analysis ◽  
Twitter Users ◽  
Negative Sentiment ◽  
Collection Program

ABSTRACT Objective: To examine the orthodontic patient experience having braces compared with Invisalign by means of a large-scale Twitter sentiment analysis. Materials and Methods: A custom data collection program was created that collected tweets containing the words “braces” or “Invisalign” for a period of 5 months. A hierarchal Naïve Bayes sentiment analysis classifier was developed to sort the tweets into five categories: positive, negative, neutral, advertisement, or not applicable. Each category was then analyzed for specific content. Results: A total of 419,363 tweets applicable to orthodontics were collected. Users posted significantly more positive tweets (61%) than they did negative tweets (39%; P ≤ .0001). There was no significant difference in the distribution of positive and negative sentiment between braces and Invisalign tweets (P = .4189). Positive orthodontics-related tweets often highlighted gratitude for a great smile accompanied with selfies. Negative orthodontic tweets frequently focused on pain. Conclusion: Twitter users expressed more positive than negative sentiment about orthodontic treatment with no significant difference in sentiment between braces and Invisalign tweets.

scholarly journals Analysis of User Sentiment of Twitter to Draft KUHP

2020 ◽  
Vol 16 (3) ◽  
pp. 273
Author(s):  
Nawang Indah Cahyaningrum ◽  
Danty Welmin Yoshida Fatima ◽  
Wisnu Adi Kusuma ◽  
Sekar Ayu Ramadhani ◽  
Muhammad Rizqi Destanto ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
The Other ◽  
Mining Methods ◽  
Twitter Users ◽  
Negative Sentiment ◽  
Bahasa Indonesia

Twitter is one of social media where its user can share many responses for a phenomenon through a tweet. This research used 5000 tweets from Twitter users in Bahasa Indonesia with keyword “RUU KUHP(Draft Law of KUHP)” from 16th of September until 22nd of September 2019. That tweets were processed using Rstudio software with sentiment analysis that is one of Text Mining methods. This research aims to classify Twitter users’ responses to RUU KUHP to be negative sentiment, poisitive negative, and neutral. Also, this research also aims to know about topics’ frequencies that were related to RUU KUHP through visualization with bar plot and also wordcloud. This research also aims to know words that are associated with the most frequent words. Form this research, can be known that Twitter users’ responses to RUU KUHP tend to have neutral sentiment that means they did not take side between agreeing or disagreeing. From this research, also can be known about 10 most frequent words, there are kpk, tunda, dpr, pasal, kesal, jokowi, presiden, masuk, ya, and sahkan. Beside that, can be known the other words that are associated with them and also their probability.

scholarly journals Analyzing the Sentiment of MOOC Discussion Posts

2019 ◽  
Vol 2 (2) ◽  
pp. 1-2
Author(s):  
Haniya Ahmed ◽  
Kenny Wong
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Positive Sentiment ◽  
Negative Sentiment

The purpose of the project is to identify common difficulties that learners may face and to understand their emotions as they progress through MOOCs. MOOC is an abbreviation for the Massive Open Online Course and the research deals with the data from ten different courses from Coursera. The data is used to extract pieces of text that students have made. Then, those certain texts are required to be sent to Google Cloud Natural Language API. This app allows users to get a sentiment analysis of a text. The main goal is to assist instructors with monitoring MOOC to make it more efficient and easier for students to progress since it assists to improve the courses.  To achieve this, the first step is to gather all the data from each of the courses. Then use programming to dump all that data into one big database. The program that is used here is called Pycharm and user is required to use python and sql to aid him in dumping the data in the database. Once the database is created, coding is done to only select out the pieces of information that are needed. These texts should be where students make comments or ask questions. Next, the data is queried to send these texts to Google Cloud Natural Language API. Here, the program breaks down all the sentences to only be just words. Then the program is going to categorize each word according to whether its connotation is positive, negative or neutral. Next, all the words are sorted according to their connotations. The overall sentiment depends on the emotion that has the highest number. If positives and negatives are all balanced out then the sentiment is neutral. Sentiment scores range from -1 to 1, where -1 is the most negative, 1 is the most positive and anywhere near 0 is neutral.  Positive sentiment scores indicate instructors that students are doing well on their course and neutral sentiment scores indicate that the course is balanced out with difficulties and easy tasks. However, negative sentiment is the most important to instructors since it indicates them that students are struggling and they need to improve the course.

scholarly journals Tracking COVID-19 Discourse on Twitter in North America: Topic Modeling and Aspect-based Sentiment Analysis (Preprint)

2020 ◽  
Author(s):  
Hyeju Jang ◽  
Emily Rempel ◽  
David Roth ◽  
Giuseppe Carenini ◽  
Naveed Z. Janjua
Keyword(s):  
Public Health ◽  
North America ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Public Health Agencies ◽  
Human In The Loop ◽  
Health Agencies ◽  
Health Experts

BACKGROUND Social media is a rich source where we can learn about people’s reactions to social issues. As COVID-19 has significantly impacted on people’s lives, it is essential to capture how people react to public health interventions and understand their concerns. OBJECTIVE We aim to investigate people’s reactions and concerns about COVID-19 in North America, especially focusing on Canada. METHODS We analyze COVID-19 related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpret the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compare timelines of topics discussed with timing of implementation of interventions, synergistically including information on people’s sentiment about COVID-19 related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compare timelines of sentiments for Asians and Canadians. RESULTS Topic modeling identified 20 topics and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions, such as physical distancing, border restrictions, hand washing, staying-home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (e.g., “vaccines”, “economy”, and “masks”) and 60 opinion terms (e.g., “infectious”- negative, and “professional”- positive), which were used for inference of sentiments of 20 selected aspects. The results showed negative sentiments related to overall outbreak, misinformation, and Asians and positive sentiments related to physical distancing. CONCLUSIONS Analyses using Natural Language Processing (NLP) techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19 related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific aspect-based sentiment analysis. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions.

Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis (Preprint)

2021 ◽  
Author(s):  
Qian Niu ◽  
Junyu Liu ◽  
Masaya Kato ◽  
Yuki Shinohara ◽  
Natsuki Matsumura ◽  
...  
Keyword(s):  
Public Health ◽  
Latent Dirichlet Allocation ◽  
Vaccine Development ◽  
Herd Immunity ◽  
Global Public Health ◽  
Prevention Measures ◽  
Public Attention ◽  
Temporal Shift ◽  
Positive Sentiment ◽  
Twitter Users

BACKGROUND The global public health and socioeconomic impacts of COVID-19 have been substantial. To achieve herd immunity, widespread use of the vaccine is required, and it is therefore critical for government and public health agencies to understand public perceptions of the vaccine to help sustain subsequent vaccinations. OBJECTIVE This study aims to explore the opinions and sentiments of tweets about COVID-19 vaccination among Twitter users in Japan, both before and at the beginning of the COVID-19 vaccination program. METHODS We collected 144,101 Japanese tweets containing COVID-19 vaccine-related keywords from Japanese Twitter users between August 1, 2020, and June 30, 2021. Specifically, we identified temporal changes in the number of tweets and key events that triggered a surge in the number of tweets. In addition, we performed sentiment analysis, and calculated the correlation between different sentiments and the number of deaths, infections, and vaccinations. We also built latent Dirichlet allocation (LDA) topic models to identify commonly discussed topics in a large sample of tweets. We also provided a word cloud of high-frequency unigram and bigram tokens as additional evidence, and conducted further analysis on three different vaccine brands. RESULTS The overall number of tweets has continued to increase since the start of mass vaccination in Japan. Sentiments were generally neutral, but negative sentiment was more significant than positive sentiment. Before and after the first vaccination in Japan, the correlations of negative/positive sentiment with death, infection, and vaccination cases changed significantly. Public concerns revolved around three themes: information on vaccine reservations and vaccinations in Japan; infection and mutation of COVID-19 in Japan; and prevention measures, vaccine development and supply, and vaccination status in other countries. Furthermore, public attention to the three brands of vaccines has a temporal shift as clinical trials move forward. CONCLUSIONS The number of tweets and changes in sentiment might be driven by major news events in relation to the COVID-19 vaccine, with negative sentiments dominating positive sentiments overall. Death and infection cases correlated significantly with negative sentiments, but the correlation fell after vaccinations began as morbidity and mortality decreased. The public’s attention to different vaccine brands had a temporal change during their clinical trial process, and although the discussion points are slightly different, the core remains effective and secure.

scholarly journals Understanding #WorldEnvironmentDay User Opinions in Twitter: A Topic-Based Sentiment Analysis Approach

2018 ◽  
Vol 15 (11) ◽  
pp. 2537 ◽  
Author(s):  
Ana Reyes-Menendez ◽  
José Saura ◽  
Cesar Alvarez-Alonso
Keyword(s):  
Public Health ◽  
Sentiment Analysis ◽  
Environment And Public Health ◽  
Key Factors ◽  
The Sustainable Development ◽  
Non Profit ◽  
Climate Risks ◽  
Development Goals ◽  
Twitter Users ◽  
High Level

The main objective of this exploratory study is to identify the social, economic, environmental and cultural factors related to the sustainable care of both environment and public health that most concern Twitter users. With 336 million active users as of 2018, Twitter is a social network that is increasingly used in research to get information and to understand public opinion as exemplified by Twitter users. In order to identify the factors related to the sustainable care of environment and public health, we have downloaded n = 5873 tweets that used the hashtag #WorldEnvironmentDay on the respective day. As the next step, sentiment analysis with an algorithm developed in Python and trained with data mining was applied to the sample of tweets to group them according to the expressed feelings. Thereafter, a textual analysis was used to group the tweets according to the Sustainable Development Goals (SDGs), identifying the key factors about environment and public health that most concern Twitter users. To this end, we used the qualitative analysis software NVivo Pro 12. The results of the analysis enabled us to establish the key factors that most concern users about the environment and public health such as climate change, global warming, extreme weather, water pollution, deforestation, climate risks, acid rain or massive industrialization. The conclusions of the present study can be useful to companies and institutions that have initiatives related to the environment and they also facilitate decision-making regarding the environment in non-profit organizations. Our findings will also serve the United Nations that will thoroughly review the 17 SDGs at the High-level Political Forum in 2019.

scholarly journals Sentiment Analysis of Microtakaful Industry: Comparison between Indonesia and Malaysia

2019 ◽  
Vol 6 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Aam Slamet Rusydiana ◽  
Irman Firmansyah ◽  
Lina Marlina
Keyword(s):  
Sentiment Analysis ◽  
Analytical Tool ◽  
Public Response ◽  
Public Sentiment ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
Industry Comparison

It is important to do research on public sentiment towards microtakaful presence in a country in order to know public response to its existence. This study aimed to determine public sentiment towards microtakaful in Indonesia and in Malaysia. Data were collected from 40 articles, journals and other writings. Data were analyzed using the software Semantria as an analytical tool in the form of text. The results showed that the assessment of existence of microtakaful in Indonesia amounted to 52% of the community showed positive sentiment, 28% indicate negative sentiment and 20% indicates a neutral sentiment. While in Malaysia that 62% showed positive sentiment, 23% negative sentiment and 15% neutral sentiment.

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