# UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages

## TL;DR

UniSent is a large, multilingual sentiment lexicon resource covering over 1000 languages, created by projecting sentiment from English using a domain adaptation method, enabling sentiment analysis in low-resource languages.

## Contribution

This work introduces UniSent, the largest multilingual sentiment lexicon to date, and proposes DomDrift, a domain adaptation method to project sentiment from Bible to Twitter data.

## Key findings

- UniSent's quality is comparable to manual resources in several languages.
- Sentiment can be reliably predicted in Twitter using UniSent and monolingual embeddings.
- UniSent enables sentiment analysis in over 1000 languages, including low-resource ones.

## Abstract

In this paper, we introduce UniSent universal sentiment lexica for $1000+$ languages. Sentiment lexica are vital for sentiment analysis in absence of document-level annotations, a very common scenario for low-resource languages. To the best of our knowledge, UniSent is the largest sentiment resource to date in terms of the number of covered languages, including many low resource ones. In this work, we use a massively parallel Bible corpus to project sentiment information from English to other languages for sentiment analysis on Twitter data. We introduce a method called DomDrift to mitigate the huge domain mismatch between Bible and Twitter by a confidence weighting scheme that uses domain-specific embeddings to compare the nearest neighbors for a candidate sentiment word in the source (Bible) and target (Twitter) domain. We evaluate the quality of UniSent in a subset of languages for which manually created ground truth was available, Macedonian, Czech, German, Spanish, and French. We show that the quality of UniSent is comparable to manually created sentiment resources when it is used as the sentiment seed for the task of word sentiment prediction on top of embedding representations. In addition, we show that emoticon sentiments could be reliably predicted in the Twitter domain using only UniSent and monolingual embeddings in German, Spanish, French, and Italian. With the publication of this paper, we release the UniSent sentiment lexica.