Sentiment analysis (SA) is commonly applied to digital textual data,
revealing insight into opinions and feelings. Many systematic reviews have
summarized existing work, but often overlook discussions of validity and
scientific practices. Here, we present an overview of reviews, synthesizing 38
systematic reviews, containing 2,275 primary studies. We devise a bespoke
quality assessment framework designed to assess the rigor and quality of
systematic review methodologies and reporting standards. Our findings show
diverse applications and methods, limited reporting rigor, and challenges over
time. We discuss how future research and practitioners can address these issues
and highlight their importance across numerous applications.

情感分析是应用于数字文本数据的常见方法，总结了现有工作，但忽视了对有效性和科学实践的讨论。我们通过综合 38 个系统性综述和 2,275 项主要研究，提出了一个针对系统评价方法和报告标准的专属质量评估框架，发现了多样的应用和方法、有限的报告严谨性以及随时间推移的挑战。我们讨论了未来研究和从业者如何解决这些问题，并强调它们在许多应用中的重要性。

数字空间中的情感分析：对评论的概述

Sentiment Analysis in Digital Spaces: An Overview of Reviews

Deep learning techniques have become the method of choice for researchers
working on algorithmic aspects of recommender systems. With the strongly
increased interest in machine learning in general, it has, as a result, become
difficult to keep track of what represents the state-of-the-art at the moment,
e.g., for top-n recommendation tasks. At the same time, several recent
publications point out problems in today's research practice in applied machine
learning, e.g., in terms of the reproducibility of the results or the choice of
the baselines when proposing new models. In this work, we report the results of
a systematic analysis of algorithmic proposals for top-n recommendation tasks.
Specifically, we considered 18 algorithms that were presented at top-level
research conferences in the last years. Only 7 of them could be reproduced with
reasonable effort. For these methods, it however turned out that 6 of them can
often be outperformed with comparably simple heuristic methods, e.g., based on
nearest-neighbor or graph-based techniques. The remaining one clearly
outperformed the baselines but did not consistently outperform a well-tuned
non-neural linear ranking method. Overall, our work sheds light on a number of
potential problems in today's machine learning scholarship and calls for
improved scientific practices in this area. Source code of our experiments and
full results are available at:
this https URL

通过系统性分析 18 种不同的算法提案，深入研究热门的 “前 n 推荐任务” 中的最佳状态，发现在这些方法中只有一种效果明显优于对照组，而其余 6 种算法经常被可比较的简单启发式方法胜过，摆露了当下机器学习研究中的潜在问题，并呼吁在这一领域改善科学实践。