Negative Tweets Research Articles

Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre- and Peri-COVID-19 Pandemic Retrospective Study.

The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19. We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users' insomnia experiences, using logistic regression. We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval. The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep.

Anti-Vaccine Discourse on Social Media: An Exploratory Audit of Negative Tweets about Vaccines and Their Posters.

As the anti-vaccination movement is spreading around the world, this paper addresses the ever more urgent need for health professionals, communicators and policy-makers to grasp the nature of vaccine mis/disinformation on social media. A one-by-one coding of 4511 vaccine-related tweets posted from the UK in 2019 resulted in 334 anti-vaccine tweets. Our analysis shows that (a) anti-vaccine tweeters are quite active and widely networked users on their own; (b) anti-vaccine messages tend to focus on the "harmful" nature of vaccination, based mostly on personal experience, values and beliefs rather than hard facts; (c) anonymity does not make a difference to the types of posted anti-vaccine content, but does so in terms of the volume of such content. Communication initiatives against anti-vaccination should (a) work closely with technological platforms to tackle anonymous anti-vaccine tweets; (b) focus efforts on mis/disinformation in three major arears (in order of importance): the medical nature of vaccines, the belief that vaccination is a tool of manipulation and control for money and power, and the "freedom of health choice" discourse against mandatory vaccination; and (c) go beyond common factual measures-such as detecting, labelling or removing fake news-to address emotions induced by personal memories, values and beliefs.

Topic modeling and sentiment analysis about Mandalika on social media using the latent Dirichlet allocation method

The rapid and widespread dissemination of information currently affects the tourism sector. One tourist area that is quite widely discussed is the Mandalika Circuit. Twitter is one platform that provides comments related to the Mandalika Circuit. The amount of information related to the Mandalika Circuit is currently not being utilized properly by managers (government or private). It causes many topics related to the Mandalika Circuit that are currently trending, and public sentiment regarding the Mandalika Circuit is unknown to the government or private sector. Ignorance can result in delays in decision making which can harm the manager. To overcome this problem, research on sentiment analysis and topic modeling related to the Mandalika Circuit was carried out. The sentiment analysis method used is SVM and for modeling using LDA. Based on the results of the sentiment analysis, 1500 tweets were obtained before doing the pre-processing process, thus getting a dataset of 500 tweets divided into 398 positive and 102 negative tweets. So it can be concluded that more Twitter users give positive than negative responses to the Mandalika Circuit. The test results show that the SVM algorithm can classify sentiment toward the Mandalika Circuit well, as indicated by the measurement of the performance of the SVM algorithm, namely 87% accuracy, 77% precision, 84.81% recall, and 98.52% specificity. These results also show that the F1 Score compares the average precision and recall, which is weighted at 80.72%.

Deciphering Latent Health Information in Social Media Using a Mixed-Methods Design.

Natural language processing techniques have increased the volume and variety of text data that can be analyzed. The aim of this study was to identify the positive and negative topical sentiments among diet, diabetes, exercise, and obesity tweets. Using a sequential explanatory mixed-method design for our analytical framework, we analyzed a data corpus of 1.7 million diet, diabetes, exercise, and obesity (DDEO)-related tweets collected over 12 months. Sentiment analysis and topic modeling were used to analyze the data. The results show that overall, 29% of the tweets were positive, and 17% were negative. Using sentiment analysis and latent Dirichlet allocation (LDA) topic modeling, we analyzed 800 positive and negative DDEO topics. From the 800 LDA topics-after the qualitative and computational removal of incoherent topics-473 topics were characterized as coherent. Obesity was the only query health topic with a higher percentage of negative tweets. The use of social media by public health practitioners should focus not only on the dissemination of health information based on the topics discovered but also consider what they can do for the health consumer as a result of the interaction in digital spaces such as social media. Future studies will benefit from using multiclass sentiment analysis methods associated with other novel topic modeling approaches.

Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling.

Since the first COVID-19 vaccine appeared, there has been a growing tendency to automatically determine public attitudes toward it. In particular, it was important to find the reasons for vaccine hesitancy, since it was directly correlated with pandemic protraction. Natural language processing (NLP) and public health researchers have turned to social media (eg, Twitter, Reddit, and Facebook) for user-created content from which they can gauge public opinion on vaccination. To automatically process such content, they use a number of NLP techniques, most notably topic modeling. Topic modeling enables the automatic uncovering and grouping of hidden topics in the text. When applied to content that expresses a negative sentiment toward vaccination, it can give direct insight into the reasons for vaccine hesitancy. This study applies NLP methods to classify vaccination-related tweets by sentiment polarity and uncover the reasons for vaccine hesitancy among the negative tweets in the Serbian language. To study the attitudes and beliefs behind vaccine hesitancy, we collected 2 batches of tweets that mention some aspects of COVID-19 vaccination. The first batch of 8817 tweets was manually annotated as either relevant or irrelevant regarding the COVID-19 vaccination sentiment, and then the relevant tweets were annotated as positive, negative, or neutral. We used the annotated tweets to train a sequential bidirectional encoder representations from transformers (BERT)-based classifier for 2 tweet classification tasks to augment this initial data set. The first classifier distinguished between relevant and irrelevant tweets. The second classifier used the relevant tweets and classified them as negative, positive, or neutral. This sequential classifier was used to annotate the second batch of tweets. The combined data sets resulted in 3286 tweets with a negative sentiment: 1770 (53.9%) from the manually annotated data set and 1516 (46.1%) as a result of automatic classification. Topic modeling methods (latent Dirichlet allocation [LDA] and nonnegative matrix factorization [NMF]) were applied using the 3286 preprocessed tweets to detect the reasons for vaccine hesitancy. The relevance classifier achieved an F-score of 0.91 and 0.96 for relevant and irrelevant tweets, respectively. The sentiment polarity classifier achieved an F-score of 0.87, 0.85, and 0.85 for negative, neutral, and positive sentiments, respectively. By summarizing the topics obtained in both models, we extracted 5 main groups of reasons for vaccine hesitancy: concern over vaccine side effects, concern over vaccine effectiveness, concern over insufficiently tested vaccines, mistrust of authorities, and conspiracy theories. This paper presents a combination of NLP methods applied to find the reasons for vaccine hesitancy in Serbia. Given these reasons, it is now possible to better understand the concerns of people regarding the vaccination process.

Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience.

Chronic dermatologic disorders can cause significant emotional distress. Sentiment analysis of disease-related tweets helps identify patients' experiences of skin disease. To analyze the expressed sentiments in tweets related to alopecia areata (AA), hidradenitis suppurativa (HS), and psoriasis (PsO) in comparison to fibromyalgia (FM). This is a cross-sectional analysis of Twitter users' expressed sentiment on AA, HS, PsO, and FM. Tweets related to the diseases of interest were identified with keywords and hashtags for one month (April, 2022) using the Twitter standard application programming interface (API). Text, account types, and numbers of retweets and likes were collected. The sentiment analysis was performed by the R "tidytext" package using the AFINN lexicon. A total of 1,505 tweets were randomly extracted, of which 243 (16.15%) referred to AA, 186 (12.36%) to HS, 510 (33.89%) to PsO, and 566 (37.61%) to FM. The mean sentiment score was -0.239 ± 2.90. AA, HS, and PsO had similar sentiment scores (p = 0.482). Although all skin conditions were associated with a negative polarity, their average was significantly less negative than FM (p < 0.0001). Tweets from private accounts were more negative, especially for AA (p = 0.0082). Words reflecting patients' psychological states varied in different diseases. "Anxiety" was observed in posts on AA and FM but not posts on HS and PsO, while "crying" was frequently used in posts on HS. There was no definite correlation between the sentiment score and the number of retweets or likes, although negative AA tweets from public accounts received more retweets (p = 0.03511) and likes (p = 0.0228). The use of Twitter sentiment analysis is a promising method to document patients' experience of skin diseases, which may improve patient care through bridging misconceptions and knowledge gaps between patients and healthcare professionals.

Negative sentiments toward novel coronavirus (COVID-19) vaccines

Addressing negative vaccine sentiments is paramount to COVID-19 prevention efforts. However, assessing population sentiments is challenging due to the desirability bias that can emerge when directly asking respondents for their opinions on vaccination. Social media data, containing people’s unfiltered thoughts, have the potential to offer valuable insights that could guide vaccine promotion messaging. We extracted one week’s (4/5–4/11, 2020) worth of COVID-19 vaccine posts on Twitter (tweets) from the U.S. (N = 208,973) and segmented tweets with negative sentiments toward COVID-19 vaccines (n = 14,794). We imputed location based on Twitter users’ self-reported state of residence. We found that states in the South had significantly higher prevalence of negative tweets compared to states in other parts of the country, and higher-income states reported lower prevalence of negative tweets. Our findings suggest the existence of negative vaccine sentiments and geographic variability in these opinions, warranting tailored vaccine promotion efforts, particularly for the southern U.S.

Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis.

In this challenging world, social media plays a vital role as it is at the pinnacle of data sharing. The advancement in technology has made a huge amount of information available for data analysis and it is on the hotlist nowadays. Opinions of the people are expressed and shared across various social media platforms like Twitter, Facebook, and Instagram. Twitter is a prodigious platform containing an ample amount of data and analyzing the data is of topmost priority. One of the most widely utilized approaches for classifying an individual’s emotions displayed in subjective data is sentiment analysis. Sentiment analysis is done using various algorithms of machine learning like Support Vector Machine, Naive Bayes, Long Short-Term Memory, Decision Tree Classifier, and many more, but this paper aims at the generalized way of performing Twitter sentiment analysis using flask environment. Flask environment provides various inbuilt functionalities to analyze the sentiments of text into three different categories: positive, negative, and neutral. Also, it makes API calls to the Twitter Developer account to fetch the Twitter data. After fetching and analyzing the data, the results get displayed on a webpage containing the percentage of positive, negative, and neutral tweets for a phrase in a pie chart. It displays the language analysis for the same phrase. Furthermore, the webpage calls attention to the tweets done on that phrase and reveals the details of the tweets. Considering the major industry runners of three different sectors namely Enterprises, Sports Apparel Industry, and Multimedia Industry, we have analyzed and compared sentiments of two different Multinational companies from each sector.

Sentiment Analysis on COVID-19 Twitter Data

Coronavirus first appeared in December 2019 in Wuhan, China which eventually lead to a catastrophic impact all over the world. The entire world had been fighting this pandemic and expressing their feelings, sharing their opinions on various social media platforms. Substantially Twitter had been the better medium to express their opinion and share updates about the situation. This paper analyzes the sentiments of the public based on positive, negative, and neutral tweets. This analysis eventually helps in the prediction of the covid-19 situation in the world. The dataset was collected from Kaggle which was uploaded by Gabriel Preda containing more than 1,70,000 tweets. Based on these tweets posted on Twitter a sentiment analysis was performed. Data Collecting, Data cleaning (Removing URL, #, @ and various types of punctuation), Tokenization, Stemming, Removing stop- words were performed, and to find the polarity, two types of analyzers were used that is TextBlob and Afinn. 1,79,108 tweets were manually analyzed and comparing it with both the analyzers shows Afinn is more accurate. To evaluate the accuracy a few Machine learning Algorithms had been applied (Logistic Regression, Naive Bayes, Decision Tree, and Linear Regression) for predicting the sentiment of the tweet.

An Investigation into Indonesian Students' Opinions on Educational Reforms through the Use of Machine Learning and Sentiment Analysis

An anti-Covid-19 plan with social restrictions forced all Indonesian educational institutions to implement online learning in 2020. Strategy in early 2022, a new policy brought back online learning methods. Because of the rapid change and short adaptation period, online learning, which had been accepted as a solution for approximately two years, has become controversial. There were a variety of reactions in society, particularly on social media, after the rapid shift from face-to-face learning to online learning. This study will quantify text sentiment expressed on social media through machine learning. This study used SVM, RF, DT, LR, and k-nearest neighbors to develop a sentiment analysis model for use in sentiment research (KNN). The SVM- and RF-based sentiment analysis models outperform the others in cross-validation tests using data from the same Twitter social media site. Furthermore, RF can classify public opinion into three groups: positive, negative, and neutral, with a low error rate. The f1 values of our KNN-based model were measured at 75%, 65%, and 87% for negative, neutral, and positive tweets, respectively, which are slightly more accurate than previous studies with the same method and purpose.

Sentiment analysis on Education transformation during Covid-19 using Arabic tweets in KSA.

The emergence of COVID-19 pandemic has changed the whole world. To prevent the spread of the virus, different precautionary measure and policies have been defined, one of them is distance learning. It has led to the educational transformation from physical education to online learning. Similarly, in KSA online education is adopted since March 2020. Inorder to extract the individual perception about the online education in KSA, twitter data was used. Arabic tweets were collected using twitter API. Furthermore, several machine learning models such as Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF), Logistic regression, K-nearest neighbors was used develop an automated sentiment analysis of online education related tweets in KSA. For feature extraction and selection N-gram and Term Frequency–Inverse Document Frequency (TF-IDF) was used. Logistic Regression achieved the highest accuracy of 69.33% for multiclass and Random Forest achieved accuracy of 80.35%. According to the dataset, most of the individuals have negative opinion about the online learning as the number of negative tweets are higher as compared to positive and neutral class

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021).

Hajj (pilgrimage) is a unique social and religious event in which many Muslims worldwide come to perform Hajj. More than two million people travel to Makkah, Saudi Arabia annually to perform various Hajj rituals for four to five days. However, given the recent outbreak of the coronavirus (COVID-19) and its variants, Hajj in the last 2 years 2020–2021 has been different because pilgrims were limited down to a few thousand to control and prevent the spread of COVID-19. This study employs a deep learning approach to investigate the impressions of pilgrims and others from within and outside the Makkah community during the 1442 AH Hajj season. Approximately 4,300 Hajj-related posts and interactions were collected from social media channels, such as Twitter and YouTube, during the Hajj season Dhul-Hijjah 1–13, 1442 (July 11–23, 2021). Convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods were utilized to investigate people’s impressions from the collected data. The CNN-LSTM approach showed superior performance results compared with other widely used classification models in terms of F-score and accuracy. Findings revealed significantly positive sentiment rates for tweets collected from Mina and Arafa holy sites, with ratios exceeding 4 out of 5. Furthermore, the sentiment analysis (SA) rates for tweets about Hajj and pilgrims varied during the days of Hajj. Some were classified as positive tweets, such as describing joy at receiving the days of Hajj, and some were negative tweets, such as expressing the impression about the hot weather and the level of satisfaction for some services. Moreover, the SA of comments on several YouTube videos revealed positive classified comments, including praise and supplications, and negative classified comments, such as expressing regret that the Hajj was limited to a small number of pilgrims.

Carbon neutrality vs. neutralité carbone: A comparative study on French and English users’ perceptions and social capital on Twitter

Carbon neutrality is one of the most critical global concerns at present. As one of the largest social media, Twitter is used widely by individuals, organisations, and government agencies to share their comments and perceptions on carbon neutrality. This study collected 26425 English and 20331 French tweets to compare the differences between French and English tweets. Social network analysis found that users in the French social networks interacted more frequently than the English ones. The geodesic analysis evidenced that the connection of any two users required about five intermediate users on average in French networks, while English ones required seven intermediate users. The modularity metrics of the English network were higher, indicating that users in English networks did not communicate with different clusters and people in carbon neutrality issues. In addition, the French network of carbon neutrality activists comprised politicians, government agencies, journalists, NGOs, and companies, while those in the English network mainly included companies, media, and politicians. Sentiment analysis and independent samples t-test have confirmed that despite the types of activists and the interactions between clusters being different, negative Tweets were more than positive ones in English and French networks, especially in French networks. It may be caused by people’s dissatisfaction with the government’s current carbon neutrality policy. By analysing the social pattern on Twitter, the research results allow people to know more about the means to enhance carbon-neutral knowledge sharing, which has the policy and social significance for addressing climate change.

Understanding public perspectives on fracking in the United States using social media big data

ABSTRACT People’s attitudes towards hydraulic fracturing (fracking) can be shaped by socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information. Existing research typically conducts surveys and interviews to study public attitudes towards fracking among a small group of individuals in a specific geographic area, where limited samples may introduce bias. Here, we compiled geo-referenced social media big data from Twitter during 2018–2019 for the entire United States to present a more holistic picture of people’s attitudes towards fracking. We used a multiscale geographically weighted regression (MGWR) to investigate county-level relationships between the aforementioned factors and percentages of negative tweets concerning fracking. Results indicate spatial heterogeneity and varying scales of those associations. Counties with higher median household income, larger African American populations, and/or lower educational level are less likely to oppose fracking, and these associations show global stationarity in all contiguous US counties. Eastern and Central US counties with higher unemployment rates, counties east of the Great Plains with less fracking sites nearby, and Western and Gulf Coast region counties with higher health insurance enrolments are more likely to oppose fracking activities. These three variables show clear East-West geographical divides in influencing public perspective on fracking. In counties across the southern Great Plains, negative attitudes towards fracking are less often vocalized on Twitter as the share of Republican voters increases. These findings have implications for both predicting public perspectives and needed policy adjustments. The methodology can also be conveniently applied to investigate public perspectives on other controversial topics.

Analyzing social media for measuring public attitudes toward controversies and their driving factors: a case study of migration

Among other ways of expressing opinions on media such as blogs, and forums, social media (such as Twitter) has become one of the most widely used channels by populations for expressing their opinions. With an increasing interest in the topic of migration in Europe, it is important to process and analyze these opinions. To this end, this study aims at measuring the public attitudes toward migration in terms of sentiments and hate speech from a large number of tweets crawled on the decisive topic of migration. This study introduces a knowledge base (KB) of anonymized migration-related annotated tweets termed as MigrationsKB (MGKB). The tweets from 2013 to July 2021 in the European countries that are hosts of immigrants are collected, pre-processed, and filtered using advanced topic modeling techniques. BERT-based entity linking and sentiment analysis, complemented by attention-based hate speech detection, are performed to annotate the curated tweets. Moreover, external databases are used to identify the potential social and economic factors causing negative public attitudes toward migration. The analysis aligns with the hypothesis that the countries with more migrants have fewer negative and hateful tweets. To further promote research in the interdisciplinary fields of social sciences and computer science, the outcomes are integrated into MGKB, which significantly extends the existing ontology to consider the public attitudes toward migrations and economic indicators. This study further discusses the use-cases and exploitation of MGKB. Finally, MGKB is made publicly available, fully supporting the FAIR principles.

Examining the Prevailing Negative Sentiments Related to COVID-19 Vaccination: Unsupervised Deep Learning of Twitter Posts over a 16 Month Period.

Despite the demonstrated efficacy, safety, and availability of COVID-19 vaccines, efforts in global mass vaccination have been met with widespread scepticism and vaccine hesitancy or refusal. Understanding the reasons for the public’s negative opinions towards COVID-19 vaccination using Twitter may help make new headways in improving vaccine uptake. This study, therefore, examined the prevailing negative sentiments towards COVID-19 vaccination via the analysis of public twitter posts over a 16 month period. Original tweets (in English) from 1 April 2021 to 1 August 2022 were extracted. A bidirectional encoder representations from transformers (BERT)-based model was applied, and only negative sentiments tweets were selected. Topic modelling was used, followed by manual thematic analysis performed iteratively by the study investigators, with independent reviews of the topic labels and themes. A total of 4,448,314 tweets were analysed. The analysis generated six topics and three themes related to the prevailing negative sentiments towards COVID-19 vaccination. The themes could be broadly understood as either emotional reactions to perceived invidious policies or safety and effectiveness concerns related to the COVID-19 vaccines. The themes uncovered in the present infodemiology study fit well into the increasing vaccination model, and they highlight important public conversations to be had and potential avenues for future policy intervention and campaign efforts.

Perceptions and Discussions of Snus on Twitter: Observational Study.

BackgroundWith the increasing popularity of snus, it is essential to understand the public perception of this oral tobacco product. Twitter—a popular social media platform that is being used to share personal experiences and opinions—provides an ideal data source for studying the public perception of snus.ObjectiveThis study aims to examine public perceptions and discussions of snus on Twitter.MethodsTwitter posts (tweets) about snus were collected through the Twitter streaming application programming interface from March 11, 2021, to February 26, 2022. A temporal analysis was conducted to examine the change in number of snus-related tweets over time. A sentiment analysis was conducted to examine the sentiments of snus-related tweets. Topic modeling was applied to tweets to determine popular topics. Finally, a keyword search and hand-coding were used to understand the health symptoms mentioned in snus-related tweets.ResultsThe sentiment analysis showed that the proportion of snus-related tweets with a positive sentiment was significantly higher than the proportion of negative sentiment tweets (4341/11,631, 37.32% vs 3094/11,631, 26.60%; P<.001). The topic modeling analysis revealed that positive tweets focused on snus’s harm reduction and snus use being an alternative to smoking, while negative tweets focused on health concerns related to snus. Mouth and respiratory symptoms were the most mentioned health symptoms in snus-related tweets.ConclusionsThis study examined the public perception of snus and popular snus-related topics discussed on Twitter, thus providing a guide for policy makers with regard to the future formulation and adjustment of tobacco regulation policies.

Tweets on a horror movie: An investigation into relationships between sentiment strength, cognitive language and tweet virality

This article studies how sentiment strength and cognitive language may influence the levels of tweet virality. A total of 11,381 tweets about a horror movie (‘Mother!’) were collected. Based on the definitions of two independent variables: sentiment strength and cognitive language use, and the dependent variable: tweet virality, the data descriptive statistics and analysis of variance (ANOVA) analysis were applied to reveal the relationships between tweet virality and sentiment and cognitive factors. The results indicate that a high tweet virality is associated with either a lower level of sentiment strength or/and a higher level of cognitive language use by a statistically significant margin. This finding is more evident for negative tweets. The study findings help improve the understanding about sentimental and cognitive factors impacting tweet virality and guide the movie industry to improve marketing movie content to achieve high virality on social media. The conclusions can also be applied to other industries, government agencies, organisations and individuals who intend to quickly disseminate specific information on social media platforms.

Analysis of Public Agenda during Covid-19 Pandemics Based on Turkish and English Tweets Using Nonnegative Matrix Factorization and Hypothesis Testing

In this study, Turkish and English tweets through Twitter Application Program Interface (API) between 1-31 January 2021 are analyzed with respect to Covid-19. The collected tweets are preprocessed, labeled with the Vader Sentiment library, and then analyzed by topic modeling with Nonnegative Matrix Factorization. The analysis show that the most frequently mentioned word is “vaccine/aşı” after “Covid”. The topics modelled in the study are grouped into themes and the themes are seen to be similar in both languages, which means that the Turkish and world agenda are not very different in terms of themes in pandemics. Moreover, hypothesis tests are conducted to understand whether language and time period are related to sentiment class. The results show that the Turkish people are more neutral about the Covid-19 issue than other people in the world during the given period of time. Moreover, independent of the language, there are more negative and neutral tweets in the first half of January 2021, whereas there are more positive tweets in the second half of the month. To the best of our knowledge, this is the first study to analyze Covid-19 related tweets in two languages to compare the local and global agendas using topic modeling, sentiment analysis, and hypothesis testing methods.

Analysis of Public Sentiment on COVID-19 Vaccination Using Twitter

Social media has become a vital platform for individuals, organizations, and governments worldwide to communicate and express their views. During the coronavirus disease 2019 (COVID-19) pandemic, social media sites play a crucial role in people communicating, sharing, and expressing their perceptions on various topics. Analyzing such textual data can improve the response time of governments and organizations to act on alarming issues. This study aims to perform sentiment analysis on the subject of COVID-19 vaccination, perform temporal and spatial analyses of the textual data, and find the most frequently discussed topics that may help organizations bring awareness to those topics. In this work, the sentiment analysis of tweets was performed using 14 different machine learning classifiers and natural language processing (NLP). Lexicon-based TextBlob and Vader are used for annotating the data. A natural language toolkit is used for preprocessing of textual data. Our analysis observed that unigram models outperform bigram and trigram models for all four datasets. Models using term frequency-inverse document frequency (TF-IDF) have higher accuracy than models using count vectorizer. In the count vectorizer class, logistic regression has the best average accuracy with 91.925%. In the TF-IDF class, logistic regression has the best average accuracy of 92%; logistic regression has the highest average recall, F1-score, and ten cross-validation scores, and a ridge classifier has the highest average precision. The unigram models show a standard deviation (SD) of less than 1 for all classifiers except for the Gaussian Naïve Bayes showing 1.18. The experimental results reveal the dates and times in which most positive, negative, and neutral tweets are posted.