The modern society runs over the social media for their most time of every day. The web users spend their most time in social media and they share many details with their friends. Such information obtained from their chat has been used in several applications. The sentiment analysis is the one which has been applied with Twitter data set toward identifying the emotion of any user and based on those different problems can be solved. Primarily, the data as of the Twitter database is preprocessed. In this step, tokenization, stemming, stop word removal, and number removal are done. The proposed automated learning with CA-SVM based sentiment analysis model reads the Twitter data set. After that they have been processed to extract the features which yield set of terms. Using the terms, the tweets are clustered using TGS-K means clustering which measures Euclidean distance according to different features like semantic sentiment score (SSS), gazetteer and symbolic sentiment support (GSSS), and topical sentiment score (TSS). Further, the method classifies the tweets according to support vector machine (CA-SVM) which classifies the tweet according to the support value which is measured based on the above two measures. The attained results are validated utilizing k-fold cross-validation methodology. Then, the classification is performed by utilizing the Balanced CA-SVM (Deep Learning Modified Neural Network). The results are evaluated and compared with the existing works. The Proposed model achieved 92.48 % accuracy and 92.05% sentiment score contrasted with the existing works.

Safety living in the society is the greatest challenge due to COVID-19. Transportation has a crucial role in ensuring the safety of society. To bring back the normality of the society after COVID-19, proper wearing of a mask and strict distancing can play a vital role in the solutions of environment and health. Hence the development of smart and riskless transport with active surveillance of passengers to enforcing the wear of mask and maintaining the social distance can be attained through deep learning algorithms (thermal imaging and sensor technologies). In this proposed work, the various image patterns have analyzed using deep learning algorithms. Passengers' health and count inside the bus are measured using sensor technologies. Hence the development of this model has capable of ensuring social distancing among the people and avoiding the crowd for riskless transport.

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