TY - GEN
T1 - Research on the Impact of ESG text on Corporate Value Based on Implicit Semantic Sentiment Analysis
AU - Gan, Yilu
AU - Liu, Jingxuan
AU - Huang, Sida
AU - Hu, Bintao
AU - Dong, Yuji
AU - Liu, Hengyan
AU - Zhang, Wenzhang
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Amid the global surge in interest in ESG disclosure, prior studies have predominantly focused on structured ESG information while neglecting implicit semantic sentiments - the latent subtle emotions and tone embedded within textual narratives. To address this gap, we propose a Knowledge Graph Driven Sentiment (KGDS) model, which employs a hybrid deep learning architecture comprising text-processing neural net works and a graph convolutional network to quantify the implicit sentiments embedded in ESG disclosure documents. Using a sample of 500 firms spanning the period from 2017 to 2022, our empirical analysis investigates the impact of these implicit sentiments on corporate market value, proxied by Tobin's Q. Our analysis reveals three key findings. First, positive implicit sentiment positively affects Tobin's Q, while negative implicit sentiment has the opposite effect, with these sentiment effects often surpassing the valuation impacts of explicit ESG performance and firm characteristics. Second, the sentiment effects exhibit pronounced industry heterogeneity, with firms in the financial and energy sectors being particularly sensitive to sentiment signals. Third, our results provide no evidence of an 'excessive positivity saturation effect'. SHAP-based explain-ability analysis shows that implicit sentiment ranks as the second most influential factor in determining market valuation, surpassed only by firm size. Theoretically, this study introduces a novel analytical framework: 'textual sentiment → investor perception → firm action → value feedback'. This framework offers a distinct perspective on ESG analysis, shifting the focus away from purely quantitative ESG metrics to incorporate qualitative textual elements. Practically, our findings suggest that securities authorities should incorporate advanced textual sentiment analysis into ESG disclosure guidelines to enhance the detection of greenwashing practices, and listed companies should strategically tailor the narrative style of their ESG reports.
AB - Amid the global surge in interest in ESG disclosure, prior studies have predominantly focused on structured ESG information while neglecting implicit semantic sentiments - the latent subtle emotions and tone embedded within textual narratives. To address this gap, we propose a Knowledge Graph Driven Sentiment (KGDS) model, which employs a hybrid deep learning architecture comprising text-processing neural net works and a graph convolutional network to quantify the implicit sentiments embedded in ESG disclosure documents. Using a sample of 500 firms spanning the period from 2017 to 2022, our empirical analysis investigates the impact of these implicit sentiments on corporate market value, proxied by Tobin's Q. Our analysis reveals three key findings. First, positive implicit sentiment positively affects Tobin's Q, while negative implicit sentiment has the opposite effect, with these sentiment effects often surpassing the valuation impacts of explicit ESG performance and firm characteristics. Second, the sentiment effects exhibit pronounced industry heterogeneity, with firms in the financial and energy sectors being particularly sensitive to sentiment signals. Third, our results provide no evidence of an 'excessive positivity saturation effect'. SHAP-based explain-ability analysis shows that implicit sentiment ranks as the second most influential factor in determining market valuation, surpassed only by firm size. Theoretically, this study introduces a novel analytical framework: 'textual sentiment → investor perception → firm action → value feedback'. This framework offers a distinct perspective on ESG analysis, shifting the focus away from purely quantitative ESG metrics to incorporate qualitative textual elements. Practically, our findings suggest that securities authorities should incorporate advanced textual sentiment analysis into ESG disclosure guidelines to enhance the detection of greenwashing practices, and listed companies should strategically tailor the narrative style of their ESG reports.
KW - ESG disclosure
KW - Greenwashing
KW - Implicit sentiment
KW - Industry heterogeneity
KW - Knowledge graph-driven sentiment model (KGDS)
KW - Tobin's Q
UR - https://www.scopus.com/pages/publications/105020905003
U2 - 10.1109/CCAI65422.2025.11189430
DO - 10.1109/CCAI65422.2025.11189430
M3 - Conference Proceeding
AN - SCOPUS:105020905003
T3 - 2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025
SP - 1005
EP - 1010
BT - 2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Computer Communication and Artificial Intelligence, CCAI 2025
Y2 - 23 May 2025 through 25 May 2025
ER -