Differentiating users by language and location estimation in sentiment analisys of informal text during major public events
Milagros Fernández‐Gavilanes, Jonathan Juncal-Martínez, Silvia García-Méndez, Enrique Costa‐Montenegro, Francisco Javier González-Castaño · Expert Systems with Applications · 2018
In recent years, social media have been intensively analyzed using sentiment analysis in order to support marketing campaigns.However, when monitoring major public events, social media users' behavior may be strongly biased by the actions of the characters in an event and by a sense of group belonging, typically linked to a specific geographical area.For an event with worldwide impact, we describe a novel solution to automatically assessing the engagement of social media users that combines a location estimation procedure with an unsupervised sentiment analysis technique.The location estimation procedure, which is content-agnostic and applies a network model based on follower accounts, is competitive with previous solutions or outperforms them.The unsupervised sentiment analysis technique, based on language semantics, achieves quite satisfactory results, bearing in mind that, unlike supervised systems, it does not require domain-specific training using annotated text.As far as we are aware, this is the first time these techniques have been implemented jointly.We demonstrate that our solution is coherent with the intrinsic predisposition of users towards the actions of the characters in an event and with a sense of group belonging.