---
format: "aidr-story-markdown/v1"
id: "468fbd02e69f3d0e53dd6da65dad014c32711f7cfc0e8c42e2f1bd400f4075b5"
canonical_url: "https://aidr.today/468fbd02?lang=en"
title: "Fine-Grained Emotion Classification from Mobile App Reviews: An Empirical Study with Large Language Models"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-10-06T04:00:00.000Z"
category: "Research"
topics: ["llm"]
source_urls: ["https://arxiv.org/abs/2610.03802"]
summary: "The paper investigates how large language models can be used for automatic multi-label emotion classification of mobile app reviews under severe class imbalance. It compares encoder-only fine-tuning, decoder-only prompting, and imbalance mitigation strategies using a synthetic-review generator and prompting strategy selected via an intrinsic augmentation-utility ranking. The study builds on a previously published annotation framework and human-labelled ground truth adapted from Plutchik's taxonomy."
---

# Fine\-Grained Emotion Classification from Mobile App Reviews: An Empirical Study with Large Language Models

> [Open the canonical story](<https://aidr.today/468fbd02?lang=en>)

**Published:** 2026-10-06T04:00:00.000Z
**Category:** Research
**Topics:** llm

## Summary

The paper investigates how large language models can be used for automatic multi\-label emotion classification of mobile app reviews under severe class imbalance\. It compares encoder\-only fine\-tuning, decoder\-only prompting, and imbalance mitigation strategies using a synthetic\-review generator and prompting strategy selected via an intrinsic augmentation\-utility ranking\. The study builds on a previously published annotation framework and human\-labelled ground truth adapted from Plutchik's taxonomy\.

## Sources

- [Story source](<https://arxiv.org/abs/2610.03802>)