Journal Articles (All Issues)

Abstract

This paper suggests a system for the automatic, low-cost, widespread, nonintrusive recognition of human needs that draws on a multi-layered psychological reference model and is built using various modules, such as those for data collecting, preprocessing, feature extraction, and contextualization. The reference model consists of various folding of SVM models to assess people's environments in relation to various aspects of their lives during any subjective event or toward emerging topics at anytime and anywhere using their publicly available social media content. It also measures people's satisfaction levels. Various textual, psychological, semantic, lexicon-based, and Twitter-specific elements are assessed for their predictive abilities. We test and compare the performance of different folding in order to offer benchmark results. Our findings attest to the usefulness of the created reference model. This work is used to identify citizen needs. Finally with the cross validation values found with the accuracy of 10 folds then the correct and more accurate model was predicted. Dataset used for this research is the question and answer discussed in the Council of States in each session which is available in kaggle for research purposes.

References

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