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EmotionLP: NLP for Subjectivity and Emotion

Sentiment analysis and emotion AI technologies apply machine learning to extract subjective evaluations from text and speech. They complement other information extraction — entities, topics, relationships, facts, and events — by adding an “affective” dimension to analyses. Let’s call this field EmotionLP: Natural Language Processing applied to model, understand, and generate emotion-rich text and speech.

Presenter Seth Grimes will survey sentiment, emotion, and intent analysis research, technologies, and applications. We will look at the representation of text-extracted emotion and at prevailing aspect-based, domain-specific sentiment, and emotion models.

We will look at data, training, and model deployment with particular attention to the analysis of emotion in reviews, chat, and healthcare text.

Deep Learning for Legal NLP

Recent advances in deep learning present awesome new opportunities to do sophisticated natural language processing on legal texts: the texts that underly...