Ni Yingying, Ni Wei
School of Media & Communication Shanghai Jiao Tong University, Shanghai, China.
Department of Critical Care Medicine, Sir Run Run Shaw Hospital, Hangzhou, Zhejiang, China.
Front Psychol. 2024 Dec 20;15:1490796. doi: 10.3389/fpsyg.2024.1490796. eCollection 2024.
This study proposes an emotion correlation-enhanced sentiment analysis model (ECO-SAM), a sentiment correlation modeling-based multi-label sentiment analysis model.
The ECO-SAM utilizes a pre-trained BERT encoder to obtain semantic embedding of input texts and then leverages a self-attention mechanism to model the semantic correlation between emotions. Additionally, it utilizes a text emotion matching neural network to make sentiment analysis for input texts.
The experiment results in public datasets demonstrate that compared to baseline models, the ECO-SAM obtains the precision score increasing by 13.33% at most, the recall score increasing by 3.69% at most, and the F1 score increasing by 8.44% at most. Meanwhile, the modeled sentiment semantics are interpretable.
The data modeled by the ECO-SAM are limited to text-only modality, excluding multi-modal data that could enhance classification performance. Additionally, the training data are not large-scale, and there is a lack of high-quality large-scale training data for fine-tuning sentiment analysis models.
The ECO-SAM is capable of effectively modeling sentiment semantics and achieving excellent classification performance in many public sentiment analysis datasets.
本研究提出了一种情感关联增强的情感分析模型（ECO-SAM），这是一种基于情感关联建模的多标签情感分析模型。
ECO-SAM利用预训练的BERT编码器获取输入文本的语义嵌入，然后利用自注意力机制对情感之间的语义关联进行建模。此外，它还利用文本情感匹配神经网络对输入文本进行情感分析。
在公共数据集上的实验结果表明，与基线模型相比，ECO-SAM的精确率得分最多提高了13.33%，召回率得分最多提高了3.69%，F1得分最多提高了8.44%。同时，所建模的情感语义是可解释的。
ECO-SAM所建模的数据仅限于纯文本模态，不包括可提高分类性能的多模态数据。此外，训练数据规模不大，缺乏用于微调情感分析模型的高质量大规模训练数据。
ECO-SAM能够有效地对情感语义进行建模，并在许多公共情感分析数据集中取得优异的分类性能。
