Neural Knowledge Transfer for Sentiment Analysis in Texts with Figurative Language
Dionysios Karamouzas, Ioannis Mademlis, Ioannis Pitas · 2022
Sentiment analysis in texts, also known as opinion mining, is a significant Natural Language Processing (NLP) task, with many applications in automated social media monitoring, customer feedback processing, e-mail scanning, etc. Despite recent progress due to advances in Deep Neural Networks (DNNs), texts containing figurative language (e.g., sarcasm, irony, metaphors) still pose a challenge to existing methods due to the semantic ambiguities they entail. In this paper, a novel setup of neural knowledge transfer is proposed for DNN-based sentiment analysis of figurative texts. It is employed for distilling knowledge from a pretrained binary recognizer of figurative language into a multiclass sentiment classifier, while the latter is being trained under a multitask setting. Thus, hints about figurativeness implicitly help resolve semantic ambiguities. Evaluation on a relevant public dataset indicates that the proposed method leads to state-of-the-art accuracy.