LLM-based scoring of 1,610 therapy sessions finds therapist empathy and exploration are followed by more client disclosure, while prior-session rapport is associated with less self-directed negative emotion—but the claimed rapport moderation is never modeled.
Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text
1 Pith paper cite this work, alongside 20 external citations. Polarity classification is still indexing.
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abstract
Recent advances in machine learning have led to computer systems that are human-like in behaviour. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behaviour. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis.
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2026 1verdicts
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Empirical Modeling of Therapist-Client Dynamics in Psychotherapy Using LLM-Based Assessments
LLM-based scoring of 1,610 therapy sessions finds therapist empathy and exploration are followed by more client disclosure, while prior-session rapport is associated with less self-directed negative emotion—but the claimed rapport moderation is never modeled.