Effectiveness of social media sentiment analysis tools with the support of emoticon/emoji

Organizations are increasingly interested in using microblogging platforms, such as Twitter, to get rapid feedback in several domains using sentiment analysis algorithms to rate, for example, whether a target audience is happy or unhappy. However, posts on microblogging platforms can differ from the source material used to train the sentiment analysis tools. For example, emojis and emoticons are increasingly employed in social media to clarify, enhance, or sometimes reverse the sentiment of a post but can be stripped out of a piece of text before it is processed. Responding to this interest, many sentiment analysis algorithms are being made available as web services, but as details of the algorithms used are not always published on the website, comparisons between web services and how well they deal with the peculiarities of microblogging posts can be difficult. To address this, a prototype web application was developed to compare the performance of nine tweet-related sentiment analysis web services and, through targeted hypotheses, to study the effect of emojis and emoticons on polarity classification. Twelve specific research test sets were created with the application, labelled by volunteers, and tested against the analysis web services with evaluation provided by two- and three-class accuracy measures. Distinct differences were found in how the web services used emoticons and emojis in assigning a positive or negative sentiment value to a tweet, with some services seeming to ignore their presence. It was found in general that web services classified polarity sensitive tweets significantly less accurately than tweets where the sentiment of the emoji/emoticon supported the sentiment of the text

Genetic Characterization of Zika Virus Strains: Geographic Expansion of the Asian Lineage

Zika virus (ZIKV) is a mosquito-transmitted flavivirus found in both Africa and Asia. Human infection with the virus may result in a febrile illness similar to dengue fever and many other tropical infections found in these regions. Previously, little was known about the genetic relationships between ZIKV strains collected in Africa and those collected in Asia. In addition, the geographic origins of the strains responsible for the recent outbreak of human disease on Yap Island, Federated States of Micronesia, and a human case of ZIKV infection in Cambodia were unknown. Our results indicate that there are two geographically distinct lineages of ZIKV (African and Asian). The virus has circulated in Southeast Asia for at least the past 50 years, whereupon it was introduced to Yap Island resulting in an epidemic of human disease in 2007, and in 2010 was the cause of a pediatric case of ZIKV infection in Cambodia. This study also highlights the danger of ZIKV introduction into new areas and the potential for future epidemics of human disease

Big data analytics: does organizational factor matters impact technology acceptance?

Abstract Ever since the emergence of big data concept, researchers have started applying the concept to various fields and tried to assess the level of acceptance of it with renown models like technology acceptance model (TAM) and it variations. In this regard, this paper tries to look at the factors that associated with the usage of big data analytics, by synchronizing TAM with organizational learning capabilities (OLC) framework. These models are applied on the construct, intended usage of big data and also the mediation effect of the OLC constructs is assessed. The data for the study is collected from the students pertaining to information technology disciplines at University of Liverpool, online programme. Though, invitation to participate e-mails are sent to 1035 students, only 359 members responded back with filled questionnaires. This study uses structural equation modelling and multivariate regression using ordinary least squares estimation to test the proposed hypotheses using the latest statistical software R. It is proved from the analysis that compared to other models, model 4 (which is constructed by using the constructs of OLC and TAM frameworks) is able to explain 44% variation in the usage pattern of big data. In addition to this, the mediation test performed revealed that the interaction between OLC dimensions and TAM dimensions on intended usage of big data has no mediation effect. Thus, this work provided inputs to the research community to look into the relation between the constructs of OLC framework and the selection of big data technology