Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification
Jeremy Barnes, Lilja Øvrelid, Erik Velldal · 2019
Neural methods for sentiment analysis have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative differences.Therefore, it is not clear what outstanding conceptual challenges for sentiment analysis remain.In this work, we attempt to discover what challenges still prove a problem for sentiment classifiers for English and to provide a challenging dataset.We collect the subset of sentences that an (oracle) ensemble of state-of-the-art sentiment classifiers misclassify and then annotate them for 18 linguistic and paralinguistic phenomena, such as negation, sarcasm, modality, etc. 1 Finally, we provide a case study that demonstrates the usefulness of the dataset to probe the performance of a given sentiment classifier with respect to linguistic phenomena.