  Sentiment analysis and emotion detection are important research topics in
natural language processing (NLP) and benefit many downstream tasks. With the
widespread application of LLMs, researchers have started exploring the
application of LLMs based on instruction-tuning in the field of sentiment
analysis. However, these models only focus on single aspects of affective
classification tasks (e.g. sentimental polarity or categorical emotions), and
overlook the regression tasks (e.g. sentiment strength or emotion intensity),
which leads to poor performance in downstream tasks. The main reason is the
lack of comprehensive affective instruction tuning datasets and evaluation
benchmarks, which cover various affective classification and regression tasks.
Moreover, although emotional information is useful for downstream tasks,
existing downstream datasets lack high-quality and comprehensive affective
annotations. In this paper, we propose EmoLLMs, the first series of
open-sourced instruction-following LLMs for comprehensive affective analysis
based on fine-tuning various LLMs with instruction data, the first multi-task
affective analysis instruction dataset (AAID) with 234K data samples based on
various classification and regression tasks to support LLM instruction tuning,
and a comprehensive affective evaluation benchmark (AEB) with 14 tasks from
various sources and domains to test the generalization ability of LLMs. We
propose a series of EmoLLMs by fine-tuning LLMs with AAID to solve various
affective instruction tasks. We compare our model with a variety of LLMs on
AEB, where our models outperform all other open-sourced LLMs, and surpass
ChatGPT and GPT-4 in most tasks, which shows that the series of EmoLLMs achieve
the ChatGPT-level and GPT-4-level generalization capabilities on affective
analysis tasks, and demonstrates our models can be used as affective annotation
tools.
