Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddings
This work addresses the challenge of emotion classification in natural language processing, offering an incremental improvement by integrating existing methods for lexicon expansion.
The paper tackled the problem of automatically extracting affective orientation by combining lexicon-based and corpus-based approaches, resulting in improved accuracy for emotion classifiers through a novel Label Propagation variant and specialized word embeddings.
There exist two main approaches to automatically extract affective orientation: lexicon-based and corpus-based. In this work, we argue that these two methods are compatible and show that combining them can improve the accuracy of emotion classifiers. In particular, we introduce a novel variant of the Label Propagation algorithm that is tailored to distributed word representations, we apply batch gradient descent to accelerate the optimization of label propagation and to make the optimization feasible for large graphs, and we propose a reproducible method for emotion lexicon expansion. We conclude that label propagation can expand an emotion lexicon in a meaningful way and that the expanded emotion lexicon can be leveraged to improve the accuracy of an emotion classifier.