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Lemotif: An Affective Visual Journal Using Deep Neural Networks

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arxiv 1903.07766 v3 pith:Y6VZ7632 submitted 2019-03-18 cs.HC cs.AIcs.CL

classification cs.HCcs.AIcs.CL
keywords journallemotifemotionsmotifsusersvisualaffectiveimage
verification ladder T0 review T1 audit T2 compute T3 formal
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We present Lemotif, an integrated natural language processing and image generation system that uses machine learning to (1) parse a text-based input journal entry describing the user's day for salient themes and emotions and (2) visualize the detected themes and emotions in creative and appealing image motifs. Synthesizing approaches from artificial intelligence and psychology, Lemotif acts as an affective visual journal, encouraging users to regularly write and reflect on their daily experiences through visual reinforcement. By making patterns in emotions and their sources more apparent, Lemotif aims to help users better understand their emotional lives, identify opportunities for action, and track the effectiveness of behavioral changes over time. We verify via human studies that prospective users prefer motifs generated by Lemotif over corresponding baselines, find the motifs representative of their journal entries, and think they would be more likely to journal regularly using a Lemotif-based app.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Computational Framework to Identify Self-Aspects in Text

    cs.CL 2025-07 unverdicted novelty 5.0 of 10

    No discovery is reported; the paper proposes a research plan for computational Self-aspect identification in text, with a small pilot study on the Social Self only.

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