Dialogic Process Analysis in Natural Language Processing: An Attempt to Describe the Sense of Reality and Meaning of Textdata

Up until now, in the field of Natural Language Processing and Computational Text Analysis Methods (CTAM) most studies focused on logical-grammatical analysis or, more recently, on content and sentiment analysis. However, there is still limited reference to the role of the discursive process: that is, how language's use shapes the reality of sense in which we live in. But how can we gain a deep knowledge and understanding of the sense of what is conveyed by a text? In order to investigate the process of sense's reality configuration, we introduce Dialogic Process Analysis. Starting from the formalization of 24 rules of natural language's use of transversal to every idiom, called Discursive Repertories, Dialogic Process Analysis allows to describe how discursive processes unravel and to trace precisely the elements that generate each specific sense's reality, which may be different even when contents and meanings are the same. Although researchers are able to denominate the Discursive Repertories, performing such a task requires specific and complex analysis expertise: that is why the application of Machine Learning models can lighten these problems. Thus, in this work we present the Dialogic Process Analysis research programme, its experimentations and results in the definition of its own Machine Learning model for textual data analysis and its future lines of development.