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An early analysis of growth dynamics for infectious diseases, like COVID-19, is needed to dissect the crucial driving factors that result in rapid disease transmission, refine the measures taken to control the pandemic and improve disease forecast. The phenomenological models are used to identify the initial climbing growth period of COVID-19 outbreak in India and have modelled 3 major epidemic growth models: Generalized logistic growth, Logistic growth and Generalized growth, to predict the growth in the total number of positive cases, daily increase in the number of positive tested cases and the daily growth rate in confirmed positive cases, dated from Apr 10, 2020, to Apr 20, 2020. The bootstrap resampling method is applied for data prediction to process the sample data, dated from Jan 31, 2020, to Apr 10, 2020, and to calculate the 3 major growth parameters: r (Rate of growth at an early stage), K (Final epidemic size) and C(Number of aggregate cases at time t), which are used to calculate confidence inte rvals which predict the future direction of the curve and increase in the number of confirmed cases with 95% accuracy for the interval Apr 10, 2020, to Apr 20, 2020. Our models predict exponential and subexponential spread rate in the number of positive cases in India from Apr 10, 2020, to Apr 20, 2020. Our findings reveal that significant measures are needed to control the transmission rate of the virus in the community, as the models predict sub-exponential growth in India.

Sentiment analysis research of public information from social networking sites has been increasing immensely in recent years. Data available at social networking sites is one of the most effective and accurate source to identify the public sentiment of any product/service. In this paper, we propose a novel localized opinion mining model based on common sense information extracted from ConceptNet ontology. The proposed methodology allows interpretation and utilization of data extracted from social media site “Twitter” to identify public opinions. This paper includes location specific, male- female specific and concept specific popularities of product. All extracted concepts are used to calculate senti_score and to build a machine learning model that classifies the user opinions as positive or negative.

Consumer reviews online may contain suggestions useful for improving the target products and services. Mining suggestions is challenging because the field lacks large labelled and balanced datasets. Furthermore, most prior studies have only focused on mining suggestions in a single domain. In this work, we introduce a novel up-sampling technique to address the problem of class imbalance, and propose a multi-task deep learning approach for mining suggestions from multiple domains. Experimental results on a publicly available dataset show that our up-sampling technique coupled with the multi-task framework outperforms state-of-the-art open domain suggestion mining models in terms of the F-1 measure and AUC.

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