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- W4320031318 abstract "Detecting emotions of the residents during disaster scenario is important for the government agencies to properly take care of its constituents. COVID-19 is a global disaster scenario that has caused unprecedented shutdowns, unemployment, death, and isolation. The behavioral and emotional health impact of COVID-19 is investigated in this study through the use of sentiment analysis and emotion recognition. The dataset is formed by collecting tweets from the seven months before COVID-19 became prevalent in March 2020 and the following seven months after. VADER sentiment analysis method was used to determine if a tweet was positive, negative, or neutral. For emotion recognition, several machine learning algorithms were evaluated and Convolutional Neural Network (CNN) Long-Short Term Memory (LSTM) performed better than the other models. Hence, CNN-LSTM was used to classify the emotion of each tweet as either anger, fear, joy, or sadness. Each tweet has a longitude and latitude stored with it that was geocoded to give the exact location, which was used to compare the states within the USA, and finally compare the USA as a whole with Canada, and Mexico. Sentiment analysis shows that all countries have experienced an increase in negative tweets. Emotion recognition shows that compared to Canada and Mexico, USA has experienced a steep drop in emotional health." @default.
- W4320031318 created "2023-02-12" @default.
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- W4320031318 date "2022-12-04" @default.
- W4320031318 modified "2023-09-23" @default.
- W4320031318 title "Emotion and Sentiment Recognition using Natural Language Processing and Machine Learning Techniques during Disaster Situation" @default.
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- W4320031318 doi "https://doi.org/10.1109/ssci51031.2022.10022125" @default.
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