Machine Translation for Accessible Multi-Language Text Analysis
This provides an accessible solution for computational scholars to include non-English languages in their analyses, addressing inclusivity in global communication research, though it is incremental as it applies existing translation tools to known methods.
The paper tackled the problem of enabling multi-language text analysis for computational scholars by showing that using Google Translate to convert texts to English before applying English-trained analytics yields adequate-to-excellent accuracy compared to source-language methods, validated over 16 languages including Spanish and Chinese.
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.