Shannon Entropy is better Feature than Category and Sentiment in User Feedback Processing

TL;DR

This paper demonstrates that Shannon Entropy is a more effective and computationally simpler feature than category and sentiment for ranking app reviews, improving prioritization accuracy.

Contribution

It introduces Shannon Entropy as a novel feature for review ranking, replacing traditional features like category and sentiment, and shows its effectiveness in improving ranking quality.

Findings

Shannon Entropy outperforms standard features in review ranking.

Entropy-based ranking achieves higher NDCG scores.

The approach is computationally less demanding.

Abstract

App reviews in mobile app stores contain useful information which is used to improve applications and promote software evolution. This information is processed by automatic tools which prioritize reviews. In order to carry out this prioritization, reviews are decomposed into features like category and sentiment. Then, a weighted function assigns a weight to each feature and a review ranking is calculated. Unfortunately, in order to extract category and sentiment from reviews, its is required at least a classifier trained in an annotated corpus. Therefore this task is computational demanding. Thus, in this work, we propose Shannon Entropy as a simple feature which can replace standard features. Our results show that a Shannon Entropy based ranking is better than a standard ranking according to the NDCG metric. This result is promising even if we require fairness by means of algorithmic bias. Finally, we highlight a computational limit which appears in the search of the best ranking.