Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons
This addresses the issue of decreased reusability and comparability in emotion analysis resources for researchers and practitioners, though it is incremental as it builds on existing representation formats.
The paper tackles the problem of incompatible emotion lexicons in sentiment analysis by proposing representation mapping methods to convert between different emotion formats, resulting in high-quality multi-lingual lexicons with near-gold quality in cross-lingual settings.
In the past years, sentiment analysis has increasingly shifted attention to representational frameworks more expressive than semantic polarity (being positive, negative or neutral). However, these richer formats (like Basic Emotions or Valence-Arousal-Dominance, and variants therefrom), rooted in psychological research, tend to proliferate the number of representation schemes for emotion encoding. Thus, a large amount of representationally incompatible emotion lexicons has been developed by various research groups adopting one or the other emotion representation format. As a consequence, the reusability of these resources decreases as does the comparability of systems using them. In this paper, we propose to solve this dilemma by methods and tools which map different representation formats onto each other for the sake of mutual compatibility and interoperability of language resources. We present the first large-scale investigation of such representation mappings for four typologically diverse languages and find evidence that our approach produces (near-)gold quality emotion lexicons, even in cross-lingual settings. Finally, we use our models to create new lexicons for eight typologically diverse languages.