Sentiment Analysis for Software Code Assessment

Ronchieri, Elisabetta; Juric, Radmila; Canaparo, Marco

doi:10.1109/nss/mic42101.2019.9059648

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…If we wished to address this, we must summarize what has been outlined in Problem 1-3 from the Introduction. Firstly, acquiring predictive inference in everyday operations of the PHO is not a straightforward task and does not have standardized techniques, which would guarantee trustworthy and reliable results [15]. These predictions depend on the way the PHO (a) define a training and testing data set, for ML classification and (b) their way of preparing twitter data, to be suitable for a chosen classification.…”

Section: The Background and Research Questionsmentioning

confidence: 99%

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Semantic Framework for Practicing Data Science in Public Health Organizations during the Covid-19 Pandemics

Juric¹,

Ronchieri²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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This paper proposes a semantic framework based on software architectures for accommodating data science practices to the needs of Public Health Organizations (PHO), during the covid-19 pandemics. The goal is to create an environment suitable for deploying data science on an ad-hoc basis, upon the selection of data generated by governments, nongovernment organizations, public databases and social media, but guided by PHO own needs and expertise. It is important to run predictions, through learning technologies, which may depend on circumstances and situations relevant for PHO in the particular moment and thus enable better decision making in the time of the pandemic. The proposed software architecture relies on its deployment within integrated development environments and plug-ins/APIs towards software tools, and libraries for (a) data gathering and preprocessing, (b) predictions with learning technologies (c) reasoning with semantic technologies and (d) including human intervention to aid in understanding the situation in which PHO questions may be answered. The illustration of the proposal is uses the sentiment analysis of twitter data relevant to covid-19 and classification of tweets with machine learning.

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Section: The Background and Research Questionsmentioning

confidence: 99%

“…Where is the proof for this? Problem 3 -The fact that we can easily compute, and run ML algorithms for getting predictive inference upon the abundance of data, does not mean that we produce trustworthy results of computing [15,16] because:…”

Section: Introductionmentioning

confidence: 99%