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SOTA Transformer and DNN short text sentiment classifiers report over 97%accuracy on narrow domains like IMDB movie reviews. Real-world performance issignificantly lower because traditional models overfit benchmarks andgeneralize poorly to different or more open domain texts. This paper introducesSentimentArcs, a new self-supervised time series sentiment analysis methodologythat addresses the two main limitations of traditional supervised sentimentanalysis: limited labeled training datasets and poor generalization. A largeensemble of diverse models provides a synthetic ground truth forself-supervised learning. Novel metrics jointly optimize an exhaustive searchacross every possible corpus:model combination. The joint optimization overboth the corpus and model solves the generalization problem. Simplevisualizations exploit the temporal structure in narratives so domain expertscan quickly spot trends, identify key features, and note anomalies overhundreds of arcs and millions of data points. To our knowledge, this is thefirst self-supervised method for time series sentiment analysis and the largestsurvey directly comparing real-world model performance on long-form narratives.

SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcs