摘要：

Abstract:

【Purpose/significance】 Microblog has become an important way of interaction between government departments and the

public. Fine-grained emotion and cause analysis on government microblog is conducive to improving government departments' public

opinion governance ability. Therefore, this paper proposes a set of Emotion-Cause Pair extraction architecture for government microb⁃

log comments.【Method/process】 This paper defines the Emotion&Cause co-occurrence sentence detection task, uses the text classifi⁃

cation model to identify the E&C co-occurrence sentence, constructs the GATECPE model to extract the Emotion-Cause Pair, and re⁃

duces the data annotation by model migration and fine-tuning.【Result/conclusion】 After validation of multiple datasets, the P value

maintained above 70% and the F1 value maintained above 60% during Emotion-Cause Pair extraction. Model fine-tuning can effec⁃

tively alleviate the effect of direct model migration. The emotional reason extraction process proposed in this paper can effectively ex⁃

tract the emotional reason of government microblog comments.【Innovation/limitation】 The source of experimental data is limited, and

error propagation exists in the two phases of Emotion&Cause co-occurrence sentence detection and Emotion-Cause Pair extraction.