ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection
Sujatha Das Gollapalli, Polina Rozenshtein, See-Kiong Ng · 2020
Accurate detection of emotions in usergenerated text was shown to have several applications for e-commerce, public well-being, and disaster management.Currently, the stateof-the-art performance for emotion detection in text is obtained using complex, deep learning models trained on domain-specific, labeled data.In this paper, we propose Emotion-Sensitive TextRank (ESTeR ), an unsupervised model for identifying emotions using a novel similarity function based on random walks on graphs.Our model combines large-scale word co-occurrence information with wordassociations from lexicons avoiding not only the dependence on labeled datasets, but also an explicit mapping of words to latent spaces used in emotion-enriched word embeddings.Our similarity function can also be computed efficiently.We study a diverse range of datasets including recent tweets related to COVID-19 to illustrate the superior performance of our model and report insights on public emotions during the on-going pandemic.