Keywords: Lexicon-based Sentiment Analysis, Natural Language Processing, Interpretability, Rule-based models
TL;DR: Linguistic rules that allow to understand the text fragments which are semantically linked and bring sentiment to a given concept of interest in a text.
Abstract: Lexicon-based Sentiment Analysis relies on sentiment dictionaries which are used to assign a sentiment polarity to the words of an input text. The overall sentiment of the text is then computed by means of a combining function, such as the word count, sum or average. In this short contribution we describe a detailed set of linguistic rules that allow to understand the text fragments which are semantically linked to a given concept of interest in a text. These heuristics have been designed in the spirit of the recent Interpretable AI trend, since they allow to understand the origin of sentiment for a specific term, providing more transparency and interpretation of the resulting analysis, and enabling the development of advanced and novel lexicon-based Sentiment Analysis approaches, which is the object of our currently on-going work.