Determining emotional profile based on microblogging analysis

First Online 30 August 2019In general, groups of people are formed because of the similarities and affinities that members have with each other. Musical preferences, soccer teams or even similar behaviours are examples of similarities and affinities that motivate group formation. In social media, identifying these affinities is a difficult task because personal information is not easily identified. In this paper we present an alternative to identifying similarities between authors and their most frequent audience in Twitter, using emotional and grammatical writing style analysis. Through this study it is possible to define the creation of an emotional profile entirely based on the interactions of people, thus allowing software like chatbots to “learn emotions” and provide emotionally acceptable responses.This work has been supported by FCT – Fundação para a Ciéncia e Tecnologia within the Project Scope: UID/CEC/00319/2019

Bundles: A Framework to Optimise Topic Analysis in Real-Time Chat Discourse.

Collaborative chat tools and large text corpora are ubiquitous in today’s world of real-time communication. As micro teams and start-ups adopt such tools, there is a need to understand the meaning (even at a high level) of chat conversations within collaborative teams. In this study, we propose a technique to segment chat conversations to increase the number of words available (19% on average) for text mining purposes. Using an open source dataset, we answer the question of whether having more words available for text mining can produce more useful information to the end user. Our technique can help microteams and start-ups with limited resources to efficiently model their conversations to afford a higher degree of readability and comprehension