Cross-lingual Opinions and Emotions Mining in Comparable Documents

TL;DR

This paper presents a language-independent method for cross-lingual opinion and emotion analysis in comparable documents, revealing source-dependent sentiment and emotion alignment across English-Arabic news articles.

Contribution

It introduces a novel approach to annotate sentiments and emotions in comparable texts without machine translation, using bilingual lexicons and statistical agreement measures.

Findings

Sentiment and emotion annotations align within the same news source.

Annotations diverge across different news sources.

Method is applicable to other language pairs.

Abstract

Comparable texts are topic-aligned documents in multiple languages that are not direct translations. They are valuable for understanding how a topic is discussed across languages. This research studies differences in sentiments and emotions across English-Arabic comparable documents. First, texts are annotated with sentiment and emotion labels. We apply a cross-lingual method to label documents with opinion classes (subjective/objective), avoiding reliance on machine translation. To annotate with emotions (anger, disgust, fear, joy, sadness, surprise), we manually translate the English WordNet-Affect (WNA) lexicon into Arabic, creating bilingual emotion lexicons used to label the comparable corpora. We then apply a statistical measure to assess the agreement of sentiments and emotions in each source-target document pair. This comparison is especially relevant when the documents originate from different sources. To our knowledge, this aspect has not been explored in prior literature. Our study includes English-Arabic document pairs from Euronews, BBC, and Al-Jazeera (JSC). Results show that sentiment and emotion annotations align when articles come from the same news agency and diverge when they come from different ones. The proposed method is language-independent and generalizable to other language pairs.