Emotion detection in Twitter posts: a rule-based algorithm for annotated data acquisition
Maria Krommyda, Anastatios Rigos, Kostas Bouklas, Angelos Amditis · 2020
Social media analysis plays a key role to the understanding of the public's opinion regarding recent events and decisions, to the design and management of advertising campaigns as well as to the planning of next steps and mitigation actions for public relationship initiatives. Significant effort has been dedicated recently to the development of data analysis algorithms that will perform, in an automated way, sentiment analysis over publicly available text. Most of the available research work, focuses on binary categorizing text as positive or negative without further investigating the emotions leading to that categorization. The current needs, however, for in-depth analysis of the available content combined with the complexity and multidimensional aspects of the human emotions and opinions have rendered such solutions obsolete. Due to these needs, currently, research is focusing on specifying the emotions and not only the sentiment expressed in a given text. This is, however, a very challenging effort due to not only the lack of annotated datasets that can be used for emotion detection in text but also the subjectivity infused in datasets that have been created based on manual annotations. A hybrid rule-based algorithm is presented in this paper, that supports the creation of a fully annotated dataset over the Plutchik's eight basic emotions. The presented algorithm takes into consideration the available emoji in the text and utilized them as objective indicators of the expressed emotion thus efficiently tackling both identified challenges. This is a full regular paper submitted to the CSCI-ISNA Symposium.