Positive Sentiment
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PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11999
Akira Matsui ◽  
Emily Chen ◽  
Yunwen Wang ◽  
Emilio Ferrara

The peer-reviewing process has long been regarded as an indispensable tool in ensuring the quality of a scientific publication. While previous studies have tried to understand the process as a whole, not much effort has been devoted to investigating the determinants and impacts of the content of the peer review itself. This study leverages open data from nearly 5,000 PeerJ publications that were eventually accepted. Using sentiment analysis, Latent Dirichlet Allocation (LDA) topic modeling, mixed linear regression models, and logit regression models, we examine how the peer-reviewing process influences the acceptance timeline and contribution potential of manuscripts, and what modifications were typically made to manuscripts prior to publication. In an open review paradigm, our findings indicate that peer reviewers’ choice to reveal their names in lieu of remaining anonymous may be associated with more positive sentiment in their review, implying possible social pressure from name association. We also conduct a taxonomy of the manuscript modifications during a revision, studying the words added in response to peer reviewer feedback. This study provides insights into the content of peer reviews and the subsequent modifications authors make to their manuscripts.

Ranjan Raj Aryal ◽  
Ankit Bhattarai

Social media is one platform where people share their opinions and views on different topics, services, or behaviors that happen around them. Since the COVID19 pandemic that started at the end of 2019, it has been a topic on which people express their sentiments. Recently, the COVID19 vaccination programs have got a lot of responses. In this paper, we have proposed two models: one based on the machine learning approach: Naive Bayes & the other based on deep learning: LSTM, whose goal is to know the sentiment of Asian region tweets towards the vaccine through sentiment analysis. The data were extracted with the help of Twitter API from March 23, 2021, till April 2, 2021. The extraction approach contains keywords with geocoding of some of the Asian countries, especially Nepal, India and Singapore. After collecting data, some preprocessing such as removing numbers, non-English & stop words, removing special characters, and hyperlinks were done. The polarity of tweets was assigned using the Text blob library. The tweets were classified into one of the three: positive, negative, or neutral. Now the data were preprocessed with the splitting of tweets into training & testing sets. Both the models were trained & tested using 10767 unique tweets. This experiment shows that a number of people in these three countries (Nepal, India and Singapore) have positive sentiment towards the vaccine and are taking the first dose of Covid19 vaccine. At last, the accuracy of the LSTM model was found to be 7% greater than that of the Naive Bayes-based model.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Shruti Gulati

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.

2021 ◽  
pp. 009365022110425
Nahema Marchal

Affective polarization—growing animosity and hostility between political rivals—has become increasingly characteristic of Western politics. While this phenomenon is well-documented through surveys, few studies investigate whether and how it manifests in the digital context, and what mechanisms underpin it. Drawing on social identity and intergroup theories, this study employs computational methods to explore to what extent political discussions on Reddit’s r/politics are affectively polarized, and what communicative factors shape these affective biases. Results show that interactions between ideologically opposed users were significantly more negative than like-minded ones. These interactions were also more likely to be cut short than sustained if one user referred negatively to the other’s political in-group. Conversely, crosscutting interactions in which one of the users expressed positive sentiment toward the out-group were more likely to attract a positive than a negative response, thus mitigating intergroup affective bias. Implications for the study of online political communication dynamics are discussed.

2021 ◽  
Vol 5 (4) ◽  
pp. 802-808
Merinda Lestandy ◽  
Abdurrahim Abdurrahim ◽  
Lailis Syafa’ah

COVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtained from 5000 COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of pre-processing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of 97.77% compared to Naïve Bayes (TF-IDF) of 80%.  

2021 ◽  
Vol 3 ◽  
Carter Floyd ◽  
Susmit S. Gulavani ◽  
James Du ◽  
Amy C. H. Kim ◽  
Jason Pappas

Student-athletes at the Division I institutions face a slew of challenges and stressors that can have negative impacts in eliciting different emotional responses during the COVID-19 pandemic. We employed machine-learning-based natural language processing techniques to analyze the user-generated content posted on Twitter of Atlantic Coast Conference (ACC) student-athletes to study changes in their sentiment as it relates to the COVID-19 crisis, major societal events, and policy decisions. Our analysis found that positive sentiment slightly outweighed negative sentiment overall, but that there was a noticeable uptick in negative sentiment in May and June 2020 in conjunction with the Black Lives Matter protests. The most commonly expressed emotions by these athletes were joy, trust, anticipation, and fear, suggesting that they used social media as an outlet to share primarily optimistic sentiments, while still publicly expressing strong negative sentiments like fear and trepidation about the pandemic and other important contemporary events. Athletic administrators, ACC coaches, support staff, and other professionals can use findings like these to guide sound, evidence-based decision-making and to better track and promote the emotional wellness of student-athletes.

Academia Open ◽  
2021 ◽  
Vol 5 ◽  
Wiji Rahayu ◽  
Wiwit Hariyanto

. This study attempts to find out how a method of Black Litterman in the formation of stock portfolios. This research was conducted on the basis of increasing the number of investors' funds in the capital market for certain stocks, showing that it increases positive sentiment on stock investments compared to other investments. The Black Litterman Model method is one of the options that can be used in the formation of portfolio. The Black Litterman model method is a method that formulates the existence of an element of return equilibrium and investor views in an investment. By using the Black Litterman Model, investors can take advantage of all available information as the basis for forming a maximum portfolio. The object of this research is Hang Seng (HSI) stock price data for the period 2017 – 2019. The research sample is 35 companies. The results of this study resulted in 10 stocks included in the Black Litterman model portfolio with the expected return on the portfolio (which consisted of 10 stocks with the Black Litterman model) of 0.062387. Where the highest proportion of returns given by Shenzhou International Group Holdings Limited (SEHK: 2313) is 23% and the expected return is 0.017933. While the lowest level is occupied by New World Development Company Limited (SEHK: 17) with a proportion of 1% and an expected return of 0.000687.

2021 ◽  
Vol 12 (No. 1) ◽  
pp. 77-108
Babatunde S. Omotosho

This paper analyses textual data mined from 37,460 reviews written by mobile banking application users in Nigeria over the period November 2012 – July 2020. On a scale of 1 to 5 (5 being the best), the average user rating for the twenty-two apps included in our sample is 3.5; with the apps deployed by non-interest banks having the highest average rating of 4.0 and those by commercial banks with national authorisation having the least rating of 3.4. Results from the sentiment analysis reveal that the share of positive sentiment words (17.8%) in the corpus more than double that of negative sentiment words (7.7%). Furthermore, we find that about 66 per cent of the emotions expressed by the users are associated with ‘trust’, ‘anticipation’, and ‘joy’ while the remaining 34 per cent relate to ‘surprise’, ‘fear’, ‘anger’, and ‘disgust’. These results imply that majority of the users are satisfied with their mobile banking experience. Finally, we find that the main topics contained in the user reviews pertain to (i) feedback on banks’ responsiveness to user complaints (ii) user experience regarding app functionalities and updates, and (iii) operational failures associated with the use of the apps. These results highlight the need for banks to continue to promote awareness of existing functionalities on their apps, educate users on how those solutions could be accessed, and respond to user feedback in a timely and effective manner.

2021 ◽  
Vol 11 (1) ◽  
Ashlynn R. Daughton ◽  
Michael J. Paul

AbstractThis work considers the use of classifiers in a downstream aggregation task estimating class proportions, such as estimating the percentage of reviews for a movie with positive sentiment. We derive the bias and variance of the class proportion estimator when taking classification error into account to determine how to best trade off different error types when tuning a classifier for these tasks. Additionally, we propose a method for constructing confidence intervals that correctly adjusts for classification error when estimating these statistics. We conduct experiments on four document classification tasks comparing our methods to prior approaches across classifier thresholds, sample sizes, and label distributions. Prior approaches have focused on providing the most accurate point estimate while this work focuses on the creation of correct confidence intervals that appropriately account for classifier error. Compared to the prior approaches, our methods provide lower error and more accurate confidence intervals.

Maurice Pitesky ◽  
Joseph Gendreau ◽  
Shayne Ramsubeik

As social media becomes an ever-increasing staple of everyday life and a growing percentage of people turn to community driven platforms as a primary source of information, the data created from these posts can provide a new source of information from which to better understand an event in near real-time. The 2018-2020 outbreak of virulent Newcastle Disease (vND) in Southern California is the third outbreak of vND in Southern California within a 50-year time span. These outbreaks are thought to be primarily driven by non-commercial poultry (i.e. backyard and game fowl) in the region. Here we employed a commercial “web crawling” tool between June of 2018 and July of 2020 which encompassed the majority of the outbreak in order to collect all available online mentions of virulent Newcastle Disease (vND) in relation to the outbreak. A total of 2,498 posts in English and Spanish were returned using a Boolean logic-based string search. While the number of posts was relatively small, their impact as measured by the number of visitors to the website and the number of people viewing the post (where provided) was much larger. Using views as a metric, Twitter was identified as the most significant source of comments over blogs, forums and other news sites. Posts with negative sentiment were found to have a larger audience relative to posts with a positive sentiment. In addition, posts with negative sentiment peaked in May of 2019 which preceded the formation of the anti-depopulation group Save Our Birds (SOB). As the usage and impact of social media grows, the ability to utilize tools to analyze social media may improve both response and outreach-based strategies for various disease outbreaks including vND in Southern California which has a large non-commercial poultry population.

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