Sentiment analysis is one of the most widely used techniques in text
analysis. Recent advancements with Large Language Models have made it more
accurate and accessible than ever, allowing researchers to classify text with
only a plain English prompt. However, "sentiment" entails a wide variety of
concepts depending on the domain and tools used. It has been used to mean
emotion, opinions, market movements, or simply a general ``good-bad''
dimension. This raises a question: What exactly are language models doing when
prompted to label documents by sentiment? This paper first overviews how
sentiment is defined across different contexts, highlighting that it is a
confounded measurement construct in that it entails multiple variables, such as
emotional valence and opinion, without disentangling them. I then test three
language models across two data sets with prompts requesting sentiment,
valence, and stance classification. I find that sentiment labels most strongly
correlate with valence labels. I further find that classification improves when
researchers more precisely specify their dimension of interest rather than
using the less well-defined concept of sentiment. I conclude by encouraging
researchers to move beyond "sentiment" when feasible and use a more precise
measurement construct.

通过测试语言模型的分类能力并结合实际应用需求，本文旨在提出一种更精确的情感测量模型，以替代广义的情感概念，从而更好地进行情感分析。

情感在语言模型中的意义是什么？

What is Sentiment Meant to Mean to Language Models?

This paper presents the ReCO, a human-curated ChineseReading Comprehension
dataset on Opinion. The questions in ReCO are opinion based queries issued to
the commercial search engine. The passages are provided by the crowdworkers who
extract the support snippet from the retrieved documents. Finally, an
abstractive yes/no/uncertain answer was given by the crowdworkers. The release
of ReCO consists of 300k questions that to our knowledge is the largest in
Chinese reading comprehension. A prominent characteristic of ReCO is that in
addition to the original context paragraph, we also provided the support
evidence that could be directly used to answer the question. Quality analysis
demonstrates the challenge of ReCO that requires various types of reasoning
skills, such as causal inference, logical reasoning, etc. Current QA models
that perform very well on many question answering problems, such as BERT, only
achieve 77% accuracy on this dataset, a large margin behind humans nearly 92%
performance, indicating ReCO presents a good challenge for machine reading
comprehension. The codes, datasets are freely available at
this https URL

本文介绍了以意见为主题的 ReCO 数据集，该数据集是目前中文阅读理解数据集中最大的，其中需要进行因果推理、逻辑推理等各种推理技能。与传统数据集不同的是，ReCO 除了提供上下文段落外，还提供支持证据，这为机器阅读理解提供了很好的挑战。

ReCO：一个关于观点的大规模中文阅读理解数据集

ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion

We investigate whether one can determine from the transcripts of U.S.
Congressional floor debates whether the speeches represent support of or
opposition to proposed legislation. To address this problem, we exploit the
fact that these speeches occur as part of a discussion; this allows us to use
sources of information regarding relationships between discourse segments, such
as whether a given utterance indicates agreement with the opinion expressed by
another. We find that the incorporation of such information yields substantial
improvements over classifying speeches in isolation.

本文研究了是否能通过美国国会辩论记录来确定演讲者是否支持或反对某项立法提案，并发现利用讨论中的相互关系信息来分类演讲可获得显著的改善。