An Automated System to Identify Sentiment from Micro-Blog Texts of Tweets

ABSTRACT

The recent trends of Internet help to produce the sentiment and emotion-based opinion at the time of conversation between human beings. During the Coronavirus disease (COVID-19) pandemic, we are spending maximum time of the day on Internet especially on social networking Web sites. The Web sites are Twitter, Facebook, and WhatsApp, which are taking a crucial role to communicate with each other in the form of messages. The messages are representing such as short-texts and micro-texts and carrying sentiment. The sentiment identification is challenging due to the length of the message. In the present paper, we are motivated to design a sentiment analysis system for Twitter micro-texts in the topic of Coronavirus disease (COVID-19). Hence, we have scrawled a dataset from Twitter using Twitter API and presented as our experimental dataset. Additionally, we have developed two state-of-the-art techniques, viz. unsupervised and supervised to build this system. The unsupervised technique helps to understand the characteristic of the dataset, whereas supervised technique assists in improving the accuracy of the system. The developed system may help to design various domain-specific applications such as annotation and emotion identification system for micro-texts in future. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.