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.

COMMUNICATING FIVE-YEAR BUDGETS FOR THE INDIAN ECONOMY: COMPARATIVE TEXT AND SENTIMENT ANALYSIS

The content on social media is full of useful information that helps in communicating people’s preferences and opinions. The various examples in this context are that people frequently express their opinions about films and other social issues using Twitter, Facebook, etc. In this work, Sentiment Analysis of the Annual Budget for five financial years, namely, 2017–2018, 2018–2019, 2019–2020, 2020–2021, and 2021–2022 was initiated with the help of Twitter. Firstly, the researcher applied Text Mining to extract the budget's text data documents and computed correlation to know the association of influential words. Then, in analysis section plotted the occurrence of the words and the accompanying word cloud. The analysis was performed employing R software. Finally, the sentiment score for each item was calculated and assessed. This research is crucial because conducting a comparative text and Sentiment Analysis of five-year budgets for the Indian economy would communicate the previously prevailing positive and negative forecasts and thinking, which will aid future policymakers in planning future budgets.

Using Sentiment Analysis to Understand What Patients Are Saying About Hand Surgeons Online

Background: Physician review websites have influence on a patient’s selection of a provider. Written reviews are subjective and difficult to quantitatively analyze. Sentiment analysis of writing can quantitatively assess surgeon reviews to provide actionable feedback for surgeons to improve practice. The objective of this study is to quantitatively analyze large subset of written reviews of hand surgeons using sentiment analysis and report unbiased trends in words used to describe the reviewed surgeons and biases associated with surgeon demographic factors. Methods: Online written and star-rating reviews of hand surgeons were obtained from healthgrades.com and webmd.com . A sentiment analysis package was used to calculate compound scores of all reviews. Mann-Whitney U tests were performed to determine the relationship between demographic variables and average sentiment score of written reviews. Positive and negative word and word-pair frequency analysis was also performed. Results: A total of 786 hand surgeons’ reviews were analyzed. Analysis showed a significant relationship between the sentiment scores and overall average star-rated reviews ( r2 = 0.604, P ≤ .01). There was no significant difference in review sentiment by provider sex; however, surgeons aged 50 years and younger had more positive reviews than older ( P < .01). The most frequently used bigrams used to describe top-rated surgeons were associated with good bedside manner and efficient pain management, whereas those with the worst reviews are often characterized as rude and unable to relieve pain. Conclusions: This study provides insight into both demographic and behavioral factors contributing to positive reviews and reinforces the importance of pain expectation management.

Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions

Researchers are interested in defining decision support systems that can act in contexts characterized by uncertainty and info-incompleteness. The present study proposes a learning model for assessing the relevance of probability, plausibility, credibility, and possibility opinions in the conditions above. The solution consists of an Artificial Neural Network acquiring input features related to the considered set of opinions and other relevant attributes. The model provides the weights for minimizing the error between the expected outcome and the ground truth concerning a given phenomenon of interest. A custom loss function was defined to minimize the Mean Best Price Error (MBPE), while the evaluation of football players’ was chosen as a case study for testing the model. A custom dataset was constructed by scraping the Transfermarkt, Football Manager, and FIFA21 information sources and by computing a sentiment score through BERT, obtaining a total of 398 occurrences, of which 85% were employed for training the proposed model. The results show that the probability opinion represents the best choice in conditions of info-completeness, predicting the best price with 0.86 MBPE (0.61% of normalized error), while an arbitrary set composed of plausibility, credibility, and possibility opinions was considered for deciding successfully in info-incompleteness, achieving a confidence score of 2.47±0.188 MBPE (1.89±0.15% of normalized error). The proposed solution provided high performance in predicting the transfer cost of a football player in conditions of both info-completeness and info-incompleteness, revealing the significance of extending the feature space to opinions concerning the quantity to predict. Furthermore, the assumptions of the theoretical background were confirmed, as well as the observations found in the state of the art regarding football player evaluation.

PUBLIC’S SENTIMENT ANALYSIS ON SHOPEE-FOOD SERVICE USING LEXICON-BASED AND SUPPORT VECTOR MACHINE

Technology field following how era keep evolving. Social media already on everyone’s daily life and being a place for writing their opinion, either review or response for product and service that already being used. Twitter are one of popular social media on Indonesia, according to Statista data it reach 17.55 million users. For online business sector, knowing sentiment score are really important to stepping up their business. The use of machine learning, NLP (Natural Processing Language), and text mining for knowing the real meaning of opinion words given by customer called sentiment analysis. Two methods are using for data testing, the first is Lexicon Based and the second is Support Vector Machine (SVM). Data source that used for sentiment analyst are from keyword ‘ShopeeFood’ and ‘syopifud’. The result of analysis giving accuracy score 87%, precision score 81%, recall score 75%, and f1-score 78%.

OP227 Exploring The Value Of Soft-Intelligence: A Case Study Using Twitter To Track Mental Health During The COVID-19 Pandemic

IntroductionThere is increasing pressure to rapidly shape policies and inform decision-making where robust evidence is lacking. This work aimed to explore the value of soft-intelligence as a novel source of evidence. We deployed an artificial intelligence based natural language platform to identify and analyze a large collection of UK tweets relating to mental health during the COVID-19 pandemic.MethodsA search strategy comprising a list of terms relating to mental health, COVID-19 and the lockdown was developed to prospectively identify relevant tweets via Twitter's advanced search application programming interface. We used a specialist text analytics platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion for qualitative analysis. All collated tweets were anonymized.ResultsWe identified 380,728 tweets from 184,289 unique users in the UK from 30 April to 4 July 2020. The average sentiment score was fifty-two percent, suggesting overall positive sentiment. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. For example, some people described how they were using the lockdown as a positive opportunity to work on their mental health, sharing helpful strategies to support others. However, many people expressed the damaging impact the pandemic (and resulting lockdown) was having on their mental health, including worsening anxiety, stress, depression, and loneliness.ConclusionsThe results suggest that soft-intelligence is potentially a useful source of evidence. The approach taken to identify and analyze this data may offer an efficient means of establishing key insights from the ‘public voice’ relating to critical health issues. However, there are still various limitations to consider concerning the technology and representativeness of the data. Future work to explore this type of evidence further, and how it might formally support decision-making processes, is recommended.This project is funded by the NIHR [(HSRIC-2016-10009)/Innovation Observatory]. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Vaping-related news events and their relationship with sentiment in the online vaping environment: A computational interrupted time series analysis with large-scale public data

Background: Vaping-related news coverage may have furthered misconceptions around the relative harms of vapes. Also, some positive opinions around vaping may be derived from misinformation, perhaps creating inimical health outcomes. Thus, we need to study how vaping-related news events (e.g. 2019 vaping illness epidemic, COVID-19) are associated with sentiment in the online vaping environment, to better understand how to promote vaping as a potential harm reduction technique for those who smoke and are unable to quit, and to minimize vape-centric misinformation that could lead to reduced health outcomes. Methods: We obtained vaping-related online data through web-scraping several online environments from August 1 2019 - April 21 2020. Sentiment analysis was performed to understand changes in sentiment in the online vaping environment in relation to vaping-related events, such as the Trump administration's planned ban on flavored vaping products, and when COVID-19 was first reported to the WHO. Results: For all online environments, we observed a statistically significant negative association of 15% (Estimate: -0.16; 95% CI: -0.29, -0.03; P: 0.01) between sentiment score and the Trump administration's move towards a ban on flavored vaping products, and a statistically significant positive association of 7% between sentiment score (Estimate: 0.07; 95% CI: 0.01, 0.14; P: 0.02) and when COVID-19 was first reported to the WHO (December 31 2019). Conclusions: News events may be related to sentiment in the online vaping environment, depending on the event. Depending on the nature of the event, we suggest that public health messaging may improve health outcomes.

Public Perception of COVID-19 Vaccines on Twitter in the United States

Background: COVID-19 vaccines play a vital role in combating the COVID-19 pandemic. Social media provides a rich data source to study public perception of COVID-19 vaccines. Objective: In this study, we aimed to examine public perception and discussion of COVID-19 vaccines on Twitter in the US, as well as geographic and demographic characteristics of Twitter users who discussed about COVID-19 vaccines. Methods: Through Twitter streaming Application Programming Interface (API), COVID-19-related tweets were collected from March 5th, 2020 to January 25th, 2021 using relevant keywords (such as "corona", "covid19", and "covid"). Based on geolocation information provided in tweets and vaccine-related keywords (such as "vaccine" and "vaccination"), we identified COVID-19 vaccine-related tweets from the US. Topic modeling and sentiment analysis were performed to examine public perception and discussion of COVID-19 vaccines. Demographic inference using computer vision algorithm (DeepFace) was performed to infer the demographic characteristics (age, gender and race/ethnicity) of Twitter users who tweeted about COVID-19 vaccines. Results: Our longitudinal analysis showed that the discussion of COVID-19 vaccines on Twitter in the US reached a peak at the end of 2020. Average sentiment score for COVID-19 vaccine-related tweets remained relatively stable during our study period except for two big peaks, the positive peak corresponds to the optimism about the development of COVID-19 vaccines and the negative peak corresponds to worrying about the availability of COVID-19 vaccines. COVID-19 vaccine-related tweets from east coast states showed relatively high sentiment score. Twitter users from east, west and southern states of the US, as well as male users and users in age group 30-49 years, were more likely to discuss about COVID-19 vaccines on Twitter. Conclusions: Public discussion and perception of COVID-19 vaccines on Twitter were influenced by the vaccine development and the pandemic, which varied depending on the geographics and demographics of Twitter users.

The impact of media sentiments on IPO underpricing

Purpose In initial public offerings (IPOs), the media plays a pivotal role by disseminating the information to the investors who generally lack the expertise to understand the information through the prospectus. Thus, media coverage can impact the investment decision of the investors and the IPO performance. Media typically covers the IPO before listing, suggesting that it may play an important role in explaining the opening price rather than the closing price on the day of listing. Therefore, this study aims to disaggregate the traditional IPO underpricing into three categories: voluntary, pre-market and post-market and provides a comparative analysis of the media sentiments impact on the traditional and disaggregated IPO underpricing. The authors’ disaggregated IPO underpricing analysis will facilitate the investors in making an effective investment strategy based on media sentiments. Design/methodology/approach The study deploys sentiment analysis using bags of n (2) grams approach to gauge the sentiments on 2,891 media articles and uses “robust-regression” technique to analyze them on a sample of 222 Indian IPOs during 2009–2018. Findings The study reports that the sentiment score is positively related to the traditional underpricing; the sentiment score is positively associated with the pre-market underpricing and does not have any significant relationship with the post-market underpricing; the number of media articles does not play a significant role in explaining the IPO underpricing. The findings highlight the presence of a semi-strong form of efficiency in the Indian IPO market. Originality/value Existing literature focuses that the role of media on IPO performance is based on the developed countries. IPO laws differ based on the countries. For instance, in India, investors can check the demand by the other categories of investors on a real-time basis. Thus, it is interesting to study whether, with such a high level of transparency, media can explain IPO performance in the Indian market. Media generally covers IPO before listing; therefore, the present study disaggregates the IPO underpricing to evaluate the role of media on the primary and secondary market separately. It will help the investors to decide when to enter and exit the market.

Decoding the global trend of “vaccine tourism” through public sentiments and emotions: does it get a nod on Twitter?

Purpose This paper aims to fill the major research gap prevalent in the tourism literature on the new form of tourism branching out from the COVID-19. While there are newspaper reports mentioning about the government’s reaction to vaccine tourism, there is no such study or report that tries to understand what the global masses feel about it; thus, a preliminary investigation of the social sentiment and emotion accruing around vaccine tourism on Twitter is carried out. Design/methodology/approach This exploratory study serves as a preliminary investigation of the social sentiment and emotion accruing around vaccine tourism on Twitter and tries to categorise them into eight basic emotions from Plutchik (1994) “wheel of emotions” as joy, disgust, fear, anger, anticipation, sadness, trust and surprise. The results are presented through data visualisation technique for analysis. The study makes use of R programming languages and the extensive packages offered on RStudio. Findings A total of 12,258 emotions were captured. It is evident that Vaccine Tourism has got maximum of positive sentiments (28.14%) which is almost double of the negative sentiment (14.05%). It is visible that the highest sentiment is “trust” (12.74%) and is followed by “fear” (8.97%). The least visible sentiment is “surprise” (4.32%). Polarity has been found for maximum tweets as positive (55.52%) which yet again surpasses negative polarity (33.7%), and neutral polarity is the least (10.67%). Research limitations/implications It can be said that people bear a positive emotion regarding vaccine tourism such as “trust” and “joy” which also denotes a positive sentiment score for testing polarity. But there are still concerns of high prices of the packages, fear-prevalent people to step out, and the uncertainty of right precautionary measures being taken still puts vaccine tourism under the radar of doubt with a fourth population having negative and neutral sentiments each. This is indicative with “fear” being the second highest emotion to the users. There are mixed emotions for vaccine tourism, but positive dominates the results. Practical implications The study attempts to see the global reaction on social media on vaccine tourism trend for giving food for thought to marketers. It can be said that Asians can be the target group. Originality/value To the best of the authors’ knowledge, there is no study that addresses the new trend of “Vaccine Tourism” or attempts to understand the emotions and sentiments of people globally.