Discriminatory Expressions to Produce Interpretable Models in Short Documents

TL;DR

This paper introduces a feature selection and ranking method to enhance interpretability of models in short social media texts, maintaining high accuracy and stability while reducing complexity.

Contribution

It proposes a novel feature selection mechanism and a ranking method to improve model interpretability in microblogging analysis, balancing performance and comprehensibility.

Findings

The method achieves better accuracy and stability across datasets.

Models are simpler and more interpretable without sacrificing performance.

The approach is effective in social media text classification tasks.

Abstract

Social Networking Sites (SNS) are one of the most important ways of communication. In particular, microblogging sites are being used as analysis avenues due to their peculiarities (promptness, short texts...). There are countless researches that use SNS in novel manners, but machine learning has focused mainly in classification performance rather than interpretability and/or other goodness metrics. Thus, state-of-the-art models are black boxes that should not be used to solve problems that may have a social impact. When the problem requires transparency, it is necessary to build interpretable pipelines. Although the classifier may be interpretable, resulting models are too complex to be considered comprehensible, making it impossible for humans to understand the actual decisions. This paper presents a feature selection mechanism that is able to improve comprehensibility by using less but more meaningful features while achieving good performance in microblogging contexts where interpretability is mandatory. Moreover, we present a ranking method to evaluate features in terms of statistical relevance and bias. We conducted exhaustive tests with five different datasets in order to evaluate classification performance, generalisation capacity and complexity of the model. Results show that our proposal is better and the most stable one in terms of accuracy, generalisation and comprehensibility.