Revealing the differences of urban parks’ services to human wellbeing based upon social media data

Cloud computing has become a de facto choice of many individuals and enterprises for computing solutions. In the last few years, many cloud providers appear in the market that offers the same services. It is a trivial job to choose an optimal service best suited for organisations in such a massive arms race of service providers. Existing consumer experience could help significantly build a holistic perception of their experiences that ultimately influence service adoption decisions. Sentiment analysis is an effective tool to understand consumer experience about the product or service. The sophisticated sentiment analysis could help businesses to gain a better insight and respond proactively to consumer issues. There are various methods for sentiment analysis that produces ideal results under different conditions. Therefore, it is very important to choose the right method to predict consumer's sentiment for a greatest result. In this paper we analyse the sentiment prediction accuracy of widely used neural network methods - recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent network (GRU). We use software as a service (SaaS) dataset having 6258 reviews. From analysis results we find that GRU outperforms the LSTM and RNN methods.

Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU

PurposeThis study analysed the effect of online negative word-of-mouth (nWOM) firestorms in the retail banking sector. By understanding negative sentiment and sentiment recovery across an entire retail banking sector, the research exposed a unique view of banking in South Africa.Design/methodology/approachThe study made use of both a sentiment and topic analysis of over 1.7 million social media posts in South Africa. The methodology made use of both NLP and human validation techniques to measure changes in social media sentiment during online firestorms. This measurement included each of South Africa's major retail banks over a twelve month period.FindingsFrom the analysis, key trigger characteristics for these firestorms (product failures, service failures, social failures and communication failures) were categorised. In addition, the average duration of a firestorm was calculated and factors that impact sentiment recovery were explored.Originality/valueThe study was located in South Africa and, unlike firm level studies, researched nWOM for the whole retail banking sector. A theoretical footprint depicting the typical anatomy of a firestorm was derived in order to aid stakeholders to be more vigilant and better equipped to provide correct intervention in such times of crisis.

The effect of nWOM firestorms on South African retail banking

BACKGROUND
Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns.


METHODS
A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States.


RESULTS
The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates.


CONCLUSIONS
Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates.

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Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms.

Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns.A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States.The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates.Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates. 

Jordan Evans

Papers

Studying social media sentiment using human validated analysis

The effect of nWOM firestorms on South African retail banking