Number Of Positive Tweets Research Articles

Exploring public sentiment and vaccination uptake of COVID-19 vaccines in England: a spatiotemporal and sociodemographic analysis of Twitter data.

Vaccination is widely regarded as the paramount approach for safeguarding individuals against the repercussions of COVID-19. Nonetheless, concerns surrounding the efficacy and potential adverse effects of these vaccines have become prevalent among the public. To date, there has been a paucity of research investigating public perceptions and the adoption of COVID-19 vaccines. Therefore, the present study endeavours to address this lacuna by undertaking a spatiotemporal analysis of sentiments towards vaccination and its uptake in England at the local authority level, while concurrently examining the sociodemographic attributes at the national level. A sentiment analysis of Twitter data was undertaken to delineate the distribution of positive sentiments and their demographic correlates. Positive sentiments were categorized into clusters to streamline comparison across different age and gender demographics. The relationship between positive sentiment and vaccination uptake was evaluated using Spearman's correlation coefficient. Additionally, a bivariate analysis was carried out to further probe public sentiment towards COVID-19 vaccines and their local adoption rates. The results indicated that the majority of positive tweets were posted by males, although females expressed higher levels of positive sentiment. The age group over 40 dominated the positive tweets and exhibited the highest sentiment polarity. Additionally, vaccination uptake was positively correlated with the number of positive tweets and the age group at the local authority level. Overall, public opinions on COVID-19 vaccines are predominantly positive. The number of individuals receiving vaccinations at the local authority level is positively correlated with the prevalence of positive attitudes towards vaccines, particularly among the population aged over 40. These findings suggest that targeted efforts to increase vaccination uptake among younger populations, particularly males, are necessary to achieve widespread vaccination coverage.

Consumer Perception of Internet Banking and Mobile Banking Using Twitter Analytics

Mobile and Internet banking have introduced a new way of monetary transactions without the need for physical presence. This research proposes to analyze the sentiments of people regarding digital transactions, Mobile and Internet banking. The explosion of Internet usage and the huge funding initiatives in electronic banking has drawn the attention of researchers towards Internet and mobile banking. This study focuses on customer value perceptions of the Internet and mobile banking in India. The recent and forecasted Digital India scheme shows high growth in e-banking in India. The demographic, attitudinal, and behavioral characteristics of mobile bank users were examined. In this study, datasets obtained from Twitter were used. After extensive and repeated analysis, it is found that both Mobile and Internet banking are well received, the number of positive Tweets, especially regarding mobile banking, is much higher than that of Internet banking. This leads to the interpretation that people find mobile banking easier and safer, especially during the ongoing COVID-19 pandemic.

Linking Twitter and survey data: asymmetry in quantity and its impact

Linked social media and survey data have the potential to be a unique source of information for social research. While the potential usefulness of this methodology is widely acknowledged, very few studies have explored methodological aspects of such linkage. Respondents produce planned amounts of survey data, but highly variant amounts of social media data. This study explores this asymmetry by examining the amount of social media data available to link to surveys. The extent of variation in the amount of data collected from social media could affect the ability to derive meaningful linked indicators and could introduce possible biases. Linked Twitter data from respondents to two longitudinal surveys representative of Great Britain, the Innovation Panel and the NatCen Panel, show that there is indeed substantial variation in the number of tweets posted and the number of followers and friends respondents have. Multivariate analyses of both data sources show that only a few respondent characteristics have a statistically significant effect on the number of tweets posted, with the number of followers being the strongest predictor of posting in both panels, women posting less than men, and some evidence that people with higher education post less, but only in the Innovation Panel. We use sentiment analyses of tweets to provide an example of how the amount of Twitter data collected can impact outcomes using these linked data sources. Results show that more negatively coded tweets are related to general happiness, but not the number of positive tweets. Taken together, the findings suggest that the amount of data collected from social media which can be linked to surveys is an important factor to consider and indicate the potential for such linked data sources in social research.

Technical Sentiment Analysis. Measuring Advantages and Drawbacks of New Products Using Social Media

The paper contributes to the literature on sentiment analysis by introducing a new knowledge-based lexicon. The lexicon, based on fundamental research and systematic practice in Engineering Design, describes the Advantages or Drawbacks (Disadvantages) of products as an effect of the interaction between artifacts and users. The paper extracts data from Twitter that report consumer conversations after the launch of new products in the videogame industry. It compares the results of a traditional sentiment analysis with the results filtered using the lexicon. We observe a drop in the number of positive tweets but a sharp increase in the informativeness of consumers’ opinions. Comments filtered using the lexicon offer a much more useful basis for understanding customers and designing new products. The paper develops several areas of potential applicability of the methodology.

Support Vector Machine for Sentiment Analysis of Nigerian Banks Financial Tweets

The rise of social media paves way for unprecedented benefits or risks to several organisations depending on how they adapt to its changes. This rise comes with a great challenge of gaining insights from these big data for effective and efficient decision making that can improve quality, profitability, productivity, competitiveness and customer satisfaction. Sentiment analysis is the field that is concerned with the classification and analysis of user generated text under defined polarities. Despite the upsurge of research in sentiment analysis in recent years, there is a dearth in literature on sentiment analysis applied to banks social media data and mostly on African datasets. Against this background, this study applied machine learning technique (support vector machine) for sentiment analysis of Nigerian banks twitter data within a 2-year period, from 1st January 2017 to 31st December 2018. After crawling and preprocessing of the data, LibSVM algorithm in WEKA was used to build the sentiment classification model based on the training data. The performance of this model was evaluated on a pre-labelled test dataset generated from the five banks. The results show that the accuracy of the classifier was 71.8367%. The precision for both the positive and negative classes was above 0.7, the recall for the negative class was 0.696 and that of the positive class was 0.741 which shows the prediction did better than chance in addition to other measures. Applying the model in predicting the sentiments of the five Nigerian banks twitter data reveals that the number of positive tweets within this period was slightly greater than the number of negative tweets. The scatter plots for the sentiments series indicated that, majority of the data falls between 0 and 100 sentiments per day, with few outliers above this range.

Psychological Analysis of Jokowi’s First 100 Days and Nawacita from Text in Twitter

In recent days, the public often uses social media such as Twitter for delivering critics; appreciation and campaign related to Government and political issues. The existence of Twitter is changing human behavior rapidly. This study aims to identify Twitter as a medium to generate public opinion concerning two political issues, the 7th Indonesian President first 100 days and public response towards his strategic plan, Nawacita. Method applied in this study is a combination of contemporary research instruments that combines technology and psychology. In this study, the authors examined conversation on Twitter by using Tracker and Algoritma Kata (AK, words algorithm). Tracker is used to collecting conversation on twitter regarding Jokowi’s first 100 days and Nawacita, whereas AK is applied to identify valence and arousal in each tweet collected by Tracker. The finding shows the domination of positive tweets in every week. However, there is a moment where the number of positive tweets was close to negative tweets. In Nawacita issue, law reformation and enforcement was the issue that has highest negative sentiment among others.