Abstract. The nation-wide lockdown imposed over India from 25th March 2020 onwards, in response to the COVID-19 pandemic, placed severe restrictions upon the industrial and transport sectors, which together form a significant chunk of anthropogenic emissions of pollutants into the atmosphere. Atmospheric concentrations of Nitrogen dioxide (NO2), carbon monoxide (CO) and aerosol optical depth (AOD) for lockdown and pre-lockdown periods were investigated with observations from Aura/OMI, Terra/MOPITT, Sentinel-5p/TROPOMI and Aqua-Terra/MODIS satellite sensors. Mean NO2 levels over India during the lockdown period showed a dip of 17 % as compared to pre-lockdown period and a decrease of 18 % against the 5-year average. Over New Delhi in particular, there was a sharp decrease of 62 % in NO2 levels as compared to 2019 and a decline by 54 % relative to the preceding 5-year period (2015–2019). Aerosol levels reduced over the country by ~ 24 % from the 5-year mean levels, with a marked reduction over the Indo-Gangetic plains region. An increase in CO levels was noticeable, probably due to its longer life-time as compared to NO2 and aerosols. This study also reports the rate of change of NO2, CO and AOD, indicating increase/decrease in pollutant emissions over the different states of India.
Sentiment analysis or opinion mining is the process of computationally identifying and categorizing opinions expressed in a piece of text, in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral. It is one of the most active research areas in natural language processing and text mining in recent years. A detailed study of the two concepts (1) Temporal sentiment analysis (2) Sentiment causal relation is presented in this paper. Temporal sentiment analysis is useful for summarizing the events based on sentiment and time. Causal relation is useful for identifying cause and effect of events and is also useful for event prediction. These two concepts when combined result in a better event prediction model that can predict the time period between the events and sentiment of upcoming events. The proposed work introduces a generalized prediction model based on temporal sentiment analysis of tweet to identify the causal relation between the events which can be used to predict the event sentiment and duration between the events. The proposed method is to be evaluated using the performance measures precision and recall. The accuracy of causal rule prediction is evaluated using parameters Mean Absolute Error (MAE) and the Root Mean Squared error (RMSE).
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