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Dual-enhanced graph convolutional networks for aspect-based financial sentiment analysis

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Abstract

Aspect-based financial sentiment analysis (ABFSA) is a challenging task at the intersection of finance and natural language processing, which aims to infer the trend of a specific entity (e.g., a company or stock) by identifying the sentiment reflected in financial-related texts, while there are a great deal of outstanding works dealing with aspect-level sentiment analysis task. However, there remains a deficiency in research explicitly targeting the ABFSA. In particular, due to the expressive differences between texts in the financial domain and social media texts, existing models lose sentiment details when mining the semantics of finance-related texts. To tackle this issue, the paper conducts a thorough analysis of the expressive features of financial texts and proposes a dual-enhanced GCN network (DEGCN) for financial sentiment analysis. DEGCN is composed of two main components: The Sentic-enhanced GCN focuses on fine-grained sentiment connections between words to overcome the loss of sentiment details caused by unified modeling, and the Domain-enhanced GCN is designed based on the characteristics of financial texts, dividing each sentence into finer categories for classification, thereby significantly reducing noise introduced by domain quantification inconsistencies. Extensive experiments and ablation studies on two public benchmark datasets demonstrate that the proposed model achieves significant improvements over previous baselines.

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Data availability

The dataset used in paper can be accessed at: SemEval-2017 Task 5 and FiQA 2018 Task 1.

Code availability

To facilitate related research, our code is publicly available at https://github.com/DEGCN/DEGCN.

Notes

  1. To facilitate related research, our code is publicly available at https://github.com/DEGCN/DEGCN.

  2. This is accomplished by calling the pipeline method using the SpaCy pre-training model en_core_web_sm Project documentation: https://spacy.io/models#quickstart

  3. https://alt.qcri.org/semeval2017/task5/.

  4. https://sites.google.com/view/fiqa/home.

References

  1. Darwich M, Noah SAM, Omar N (2020) Deriving the sentiment polarity of term senses using dual-step context-aware in-gloss matching. Info Proc Manag 57(6):102273

    Article  MATH  Google Scholar 

  2. Jung HD, Jang B (2024) Enhancing financial sentiment analysis ability of language model via targeted numerical change-related masking. IEEE Access 12:50809–50820

    Article  MATH  Google Scholar 

  3. Li Q, Li X, Du Y, Chen X (2021) ISWR: an implicit sentiment words recognition model based on sentiment propagation. In: Natural Language Processing and Chinese Computing - 10th CCF International Conference, NLPCC 2021, Qingdao, China, October 13-17, Proceedings, Part II, vol. 13029, pp. 248–259. Springer

  4. Ahmed R, Abdul-Rauf S, Latif S (2024) Leveraging large language models and prompt settings for context-aware financial sentiment analysis. In: 5th International Conference on Advancements in Computational Sciences, ICACS 2024, Lahore, Pakistan, February 19-20, pp. 1–9. IEEE

  5. Li S, Shi W, Wang J, Zhou H (2021) A deep learning-based approach to constructing a domain sentiment lexicon: a case study in financial distress prediction. Info Proc Manag 58(5):102673

    Article  MATH  Google Scholar 

  6. Alshaikh KA, Almatrafi O, Abushark YB (2024) Bert-based model for aspect-based sentiment analysis for analyzing arabic open-ended survey responses: a case study. IEEE Access 12:2288–2302

    Article  Google Scholar 

  7. Huang F, Li X, Yuan C, Zhang S, Zhang J, Qiao S (2022) Attention-emotion-enhanced convolutional LSTM for sentiment analysis. IEEE Trans Neural Networks Learn Syst 33(9):4332–4345

    Article  MATH  Google Scholar 

  8. Jin W, Zhao B, Zhang Y, Huang J, Yu H (2024) Wordtransabsa: enhancing aspect-based sentiment analysis with masked language modeling for affective token prediction. Expert Syst Appl 238:122289

    Article  Google Scholar 

  9. Huang F, Yuan C, Bi Y, Lu J, Lu L, Wang X (2022) Multi-granular document-level sentiment topic analysis for online reviews. Appl Intell 52(7):7723–7733

    Article  MATH  Google Scholar 

  10. Yin Y, Jing L, Huang F, Yang G, Wang Z (2024) MSA-GCN: multiscale adaptive graph convolution network for gait emotion recognition. Pattern Recognit. 147:110117

    Article  MATH  Google Scholar 

  11. Jin W, Zhao B, Yu H, Tao X, Yin R, Liu G (2023) Improving embedded knowledge graph multi-hop question answering by introducing relational chain reasoning. Data Min Knowl Discov 37(1):255–288

    Article  Google Scholar 

  12. Jin W, Zhao B, Zhang L, Liu C, Yu H (2023) Back to common sense: oxford dictionary descriptive knowledge augmentation for aspect-based sentiment analysis. Inf Process Manag 60(3):103260

    Article  MATH  Google Scholar 

  13. Popoola G, Abdullah K, Fuhnwi GS, Agbaje J (2024) Sentiment analysis of financial news data using TF-IDF and machine learning algorithms. In: 3rd IEEE International Conference on AI in Cybersecurity, ICAIC 2024, Houston, TX, USA, February 7-9, pp. 1–6. IEEE

  14. Lengkeek M, Knaap F, Frasincar F (2023) Leveraging hierarchical language models for aspect-based sentiment analysis on financial data. Info Proc Manag 60(5):103435

    Article  Google Scholar 

  15. Liutvinavicius M, Zubova J, Sakalauskas V (2017) Behavioural economics approach: using investors sentiment indicator for financial markets forecasting. Baltic J Modern Comput 5(3):275

    Article  Google Scholar 

  16. Chan SWK, Chong MWC (2017) Sentiment analysis in financial texts. Decis Support Syst 94:53–64

    Article  MATH  Google Scholar 

  17. Türkmen AC (2016) Sentiment extraction from financial public disclosure documents. In: Proceedings of the First Workshop on MIning DAta for Financial applicationS (MIDAS 2016) Co-located with the 2016 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2016), Riva del Garda, Italy, September 19-23, vol. 1774, pp. 67–72. CEUR-WS.org

  18. Loughran T, McDonald B (2011) When is a liability not a liability? textual analysis, dictionaries, and 10-ks. J Financ 66(1):35–65

    Article  MATH  Google Scholar 

  19. Tetlock PC (2007) Giving content to investor sentiment: The role of media in the stock market. J Financ 62(3):1139–1168

    Article  MATH  Google Scholar 

  20. Tetlock PC, Saar-Tsechansky M, Macskassy S (2008) More than words: quantifying language to measure firms’ fundamentals. J Financ 63(3):1437–1467

    Article  MATH  Google Scholar 

  21. Yazdani SF, Murad MAA, Sharef NM, Singh YP, Latiff ARA (2017) Sentiment classification of financial news using statistical features. Int J Pattern Recognit Artif Intell 31(3):1750006–1175000634

    Article  Google Scholar 

  22. Ding X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, pp. 2327–2333. AAAI Press

  23. Kim Y Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A Meeting of SIGDAT, a Special Interest Group of The ACL, pp. 1746–1751. ACL

  24. Sharaff A, Chowdhury TR, Bhandarkar S (2023) LSTM based sentiment analysis of financial news. SN Comput. Sci. 4(5):584

    Article  MATH  Google Scholar 

  25. Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, Volume 1 (Long and Short Papers), pp. 4171–4186. ACL

  26. Yang Y, Uy MCS, Huang A (2020) Finbert: a pretrained language model for financial communications. CoRR abs/2006.08097

  27. Cao Y, Tang Y, Du H, Xu F, Wei Z, Jin C (2023) Heterogeneous reinforcement learning network for aspect-based sentiment classification with external knowledge. IEEE Trans Affect Comput 14(4):3362–3375

    Article  MATH  Google Scholar 

  28. Liang B, Su H, Gui L, Cambria E, Xu R (2022) Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl-Based Syst 235:107643

    Article  Google Scholar 

  29. Cambria E, Speer R, Havasi C, Hussain A (2010) Senticnet: A publicly available semantic resource for opinion mining. In: Commonsense Knowledge, Papers from the 2010 AAAI Fall Symposium, Arlington, Virginia, USA, November 11-13. AAAI Technical Report, vol. FS-10-02. AAAI

  30. Cambria E, Havasi C, Hussain A (2012) Senticnet 2: A semantic and affective resource for opinion mining and sentiment analysis. In: Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference, Marco Island, Florida, USA, May 23-25. AAAI Press

  31. Cambria E, Olsher D, Rajagopal D (2014) Senticnet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, Québec City, Québec, Canada, pp. 1515–1521. AAAI Press

  32. Cambria E, Poria S, Bajpai R, Schuller BW (2016) Senticnet 4: A semantic resource for sentiment analysis based on conceptual primitives. In: COLING 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, December 11-16, Osaka, Japan, pp. 2666–2677. ACL

  33. Cambria E, Poria S, Hazarika D, Kwok K (2018) Senticnet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th Innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, pp. 1795–1802. AAAI Press

  34. Cambria E, Li Y, Xing FZ, Poria S, Kwok K (2020) Senticnet 6: Ensemble application of symbolic and subsymbolic AI for sentiment analysis. In: d’Aquin, M., Dietze, S., Hauff, C., Curry, E., Cudré-Mauroux, P. (eds.) CIKM ’20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, pp. 105–114. ACM

  35. Cortis K, Freitas A, Daudert T, Hürlimann M, Zarrouk M, Handschuh S, Davis B (2017) Semeval-2017 task 5: Fine-grained sentiment analysis on financial microblogs and news. In: Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval 2017, Vancouver, Canada, August 3-4, pp. 519–535. ACL

  36. Maia M, Handschuh S, Freitas A, Davis B, McDermott R, Zarrouk M, Balahur A (2018) Www’18 open challenge: Financial opinion mining and question answering. In: Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27, pp. 1941–1942. ACM

  37. Zhang C, Li Q, Song D (2019) Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, pp. 4567–4577. ACL

  38. Wu H, Zhang Z, Shi S, Wu Q, Song H (2022) Phrase dependency relational graph attention network for aspect-based sentiment analysis. Knowl-Based Syst 236:107736

    Article  MATH  Google Scholar 

  39. Wu H, Zhang Z, Shi S, Wu Q, Song H (2022) Phrase dependency relational graph attention network for aspect-based sentiment analysis. Knowl-Based Syst 236:107736

    Article  MATH  Google Scholar 

  40. Zhang Z, Zhou Z, Wang Y (2022) SSEGCN: syntactic and semantic enhanced graph convolutional network for aspect-based sentiment analysis. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, pp. 4916–4925. ACL

  41. Shang L, Xi H, Hua J, Tang H, Zhou J (2023) A lexicon enhanced collaborative network for targeted financial sentiment analysis. Info Proc Manag 60(2):103187

    Article  MATH  Google Scholar 

  42. Xiang C, Zhang J, Li F, Fei H, Ji D (2022) A semantic and syntactic enhanced neural model for financial sentiment analysis. Info Proc Manag 59(4):102943

    Article  MATH  Google Scholar 

  43. Qin C, Yu C, Meng Y, Chang J (2023) A numeral and affective knowledge enhanced network for aspect-based financial sentiment analysis. In: 35th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2023, Atlanta, GA, USA, November 6-8, pp. 926–933. IEEE

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  1. School of Social Sciences, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Ruiyang Yao

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Ruiyang Yao did all the work on the manuscript independently, including data collection, data organization, first draft writing, and manuscript revision.

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Yao, R. Dual-enhanced graph convolutional networks for aspect-based financial sentiment analysis. J Supercomput 81, 607 (2025). https://doi.org/10.1007/s11227-025-06972-x

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