We conduct an inquiry into the sociotechnical aspects of sentiment analysis
(SA) by critically examining 189 peer-reviewed papers on their applications,
models, and datasets. Our investigation stems from the recognition that SA has
become an integral component of diverse sociotechnical systems, exerting
influence on both social and technical users. By delving into sociological and
technological literature on sentiment, we unveil distinct conceptualizations of
this term in domains such as finance, government, and medicine. Our study
exposes a lack of explicit definitions and frameworks for characterizing
sentiment, resulting in potential challenges and biases. To tackle this issue,
we propose an ethics sheet encompassing critical inquiries to guide
practitioners in ensuring equitable utilization of SA. Our findings underscore
the significance of adopting an interdisciplinary approach to defining
sentiment in SA and offer a pragmatic solution for its implementation.

我们通过对 189 篇同行评审论文在应用程序、模型和数据集方面的批判性研究，对情感分析（SA）的社会技术方面进行了探究，并从中发现情感分析已成为不同的社会技术系统的重要组成部分，对社会和技术用户产生影响。我们深入社会学和技术文献，揭示了在金融、政府和医疗等领域中对这一概念的不同概念化。我们的研究揭示了情感的明确定义和框架的缺失，从而可能带来挑战和偏见。为了解决这个问题，我们提出了一份伦理表，包含关键问题，以指导从业人员确保情感分析的公平利用。我们的发现强调了在定义 SA 中的情感方面采用跨学科方法的重要性，并为其实施提供了实用的解决方案。

情感问题：拆解情感分析的关键调查

The Sentiment Problem: A Critical Survey towards Deconstructing  Sentiment Analysis

Machine translation (MT) technology has facilitated our daily tasks by
providing accessible shortcuts for gathering, elaborating and communicating
information. However, it can suffer from biases that harm users and society at
large. As a relatively new field of inquiry, gender bias in MT still lacks
internal cohesion, which advocates for a unified framework to ease future
research. To this end, we: i) critically review current conceptualizations of
bias in light of theoretical insights from related disciplines, ii) summarize
previous analyses aimed at assessing gender bias in MT, iii) discuss the
mitigating strategies proposed so far, and iv) point toward potential
directions for future work.

通过批判性地审视当前有关 MT 偏见的概念化，总结以往的性别偏见分析，讨论已提出的缓解策略，并指向未来工作的潜在方向，提出了一个统一的框架以便于未来研究。