Sentiment Analysis within the Urgency Tweets

Abstract

So far, many artificial agents have been built to detect urgency, emotions, perception, and motivation in textual data to better interact with humans. Although much work has been done on these agents yet there is a need to enhance these detection abilities so that the accuracy can be maximized. One of the most important abilities is a sense of urgency Urgencies based on emotion expressed in a statement can be identified easily by humans as they can use their cognitive abilities to detect the level of urgency from a sentence. But in the case of artificial agents, it is not that easy, they process syntax to find out the emotions in the statements. Much work has been done on the detection of emotions based on the syntax however further research is required on the detection of sentiments within the urgent textual statements. The proposed system will detect the sentiments in urgency-based tweets. It is done by data scraping from the twitter site. Data scrapping is done by use of TWINT API. After data scraping from the twitter web site, this scrapped data is preprocessed using different python libraries. Machine learning techniques (ML) like supervised learning and Natural Language Processing (NLP) is used. The system will detect the sentiments or emotions’ nature in the statements i.e. whether it is showing positive or negative sentiments.