English is the international standard of social research, but scholars are
increasingly conscious of their responsibility to meet the need for scholarly
insight into communication processes globally. This tension is as true in
computational methods as any other area, with revolutionary advances in the
tools for English language texts leaving most other languages far behind. In
this paper, we aim to leverage those very advances to demonstrate that
multi-language analysis is currently accessible to all computational scholars.
We show that English-trained measures computed after translation to English
have adequate-to-excellent accuracy compared to source-language measures
computed on original texts. We show this for three major analytics -- sentiment
analysis, topic analysis, and word embeddings -- over 16 languages, including
Spanish, Chinese, Hindi, and Arabic. We validate this claim by comparing
predictions on original language tweets and their backtranslations: double
translations from their source language to English and back to the source
language. Overall, our results suggest that Google Translate, a simple and
widely accessible tool, is effective in preserving semantic content across
languages and methods. Modern machine translation can thus help computational
scholars make more inclusive and general claims about human communication.

本文研究通过机器翻译将其他语言翻译成英文进行多语言文本分析，结果表明这种方法在情感分析，主题分析和词嵌入等分析方法上，与使用源语言进行分析的结果相当，机器翻译可以帮助计算机学者做出关于人类沟通的更具包容性和普适性的结论。

可访问的多语言文本分析的机器翻译

Machine Translation for Accessible Multi-Language Text Analysis

Text analysis of social media for sentiment, topic analysis, and other
analysis depends initially on the selection of keywords and phrases that will
be used to create the research corpora. However, keywords that researchers
choose may occur infrequently, leading to errors that arise from using small
samples. In this paper, we use the capacity for memorization, interpolation,
and extrapolation of Transformer Language Models such as the GPT series to
learn the linguistic behaviors of a subgroup within larger corpora of Yelp
reviews. We then use prompt-based queries to generate synthetic text that can
be analyzed to produce insights into specific opinions held by the populations
that the models were trained on. Once learned, more specific sentiment queries
can be made of the model with high levels of accuracy when compared to
traditional keyword searches. We show that even in cases where a specific
keyphrase is limited or not present at all in the training corpora, the GPT is
able to accurately generate large volumes of text that have the correct
sentiment.

使用 Transformer Language Models 等方法对 Yelp 评论进行语言行为学习，并利用 prompt-based queries 生成合成文本以分析特定观点，证明即使在缺乏特定关键词的情况下，这些模型也能准确产生具有正确情感的大量文本。