Abstract
Textual data is one of the most important types of data nowadays. With the use of social media like Twitter and Facebook, the amount of textual data is growing at an unimaginable speed. To fully use these data, it is very important to extract useful information from it as much as possible. While some information can be directly extracted from the text, other information needs to be discovered with an advanced method. For instance, the feelings of authors while the text was written, or the emotions that authors wanted to express. This leads to the use of sentiment analysis. Recently, sentiment analysis has drawn attention from many researchers. With the growth of online resources of textual data, the ability to detect different emotions from textual data is becoming very useful. In this paper, we present an application that can detect four distinct emotions from Twitter posts. They are joy, anger, fear, and sadness. The evaluation result shows the accuracy and applicability of our application.
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Jiang, F., Aarts, C. (2021). Emotion Detection on Twitter Textual Data. In: Lee, W., Leung, C.K., Nasridinov, A. (eds) Big Data Analyses, Services, and Smart Data. BIGDAS 2018. Advances in Intelligent Systems and Computing, vol 899. Springer, Singapore. https://doi.org/10.1007/978-981-15-8731-3_5
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DOI: https://doi.org/10.1007/978-981-15-8731-3_5
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