Skip to main content

Advertisement

Springer Nature Link
Log in

A method of constructing a fine-grained sentiment lexicon for the humanities computing of classical chinese poetry

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In Chinese classics, the sentiment attitudes or thoughts of the ancients regarding specific environments, people, and events were generally expressed in the form of poetry. Compared with previous attempts to classify the polarity of poetry, sentiment terms can be used to detect more fine-grained humanity knowledge in literary information resources. However, the existing techniques of domain sentiment lexicon construction fail to take full advantage of deep learning and linguistic knowledge, which cannot ensure the term integrity and accuracy. To this end, this work proposes a novel approach for the construction of a sentiment lexicon via the combination of supervised sentiment term extraction and classification, aiming at incorporating multi-dimensional linguistic knowledge into a two-phase deep learning model. A character-sequence labeling model for term extraction is first constructed by fusing the emotion radical features of Chinese characters, and term embedding augmentation via word knowledge is then carried out to classify the extracted terms. Experiments on Chinese poetry and its appreciation texts validate the superiority of the proposed method, and the model incorporating linguistic knowledge is found to outperform the benchmark models in different metrics. A fine-grained sentiment lexicon with two first classes, five-second classes, 15 third classes, and 14,368 domain terms and unregistered terms is constructed via hierarchical term classification, thereby contributing to the advancement of the interpretability of the humanities computing of classical Chinese poetry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The authors confirm that the datasets generated and analyzed during the current study are available in public repositories (see footnotes in the article), and all of them support the published claims and comply with field standards.

Notes

  1. http://www.wenxue100.com/.

  2. https://hanyu.baidu.com/.

  3. https://github.com/google-research/bert/blob/master/multilingual.md.

  4. https://github.com/Weiiiing/poetry-sentiment-lexicon.

References

  1. Hong L, Hou W, Wu Z, Han H (2020) A cooperative crowdsourcing framework for knowledge extraction in digital humanities–cases on Tang poetry. Aslib J Inf Manag 72(2):243–261

    Article  Google Scholar 

  2. Li G, Li J (2018) Research on sentiment classification for Tang poetry based on TF-IDF and FP-growth. In: 2018 IEEE 3rd advanced information technology, electronic and automation control conference (IAEAC), pp 630–634. IEEE

  3. Wu B, Ji J, Meng L, Shi C, Zhao HD, Li YQ (2016) Transfer learning based sentiment analysis for poetry of the Tang Dynasty and Song Dynasty. Acta Electron Sin 44(11):2780–2787

    Google Scholar 

  4. Tang Y, Wang X, Qi P, Sun Y (2020) A neural network-based sentiment analysis scheme for Tang Poetry. In: 2020 international wireless communications and mobile computing (IWCMC), pp 1783–1788. IEEE

  5. Su C, Li J, Peng Y, Chen Y (2019) Chinese metaphor sentiment computing via considering culture. Neurocomputing 352:33–41

    Article  Google Scholar 

  6. Wu S, Wu F, Chang Y, Wu C, Huang Y (2019) Automatic construction of target-specific sentiment lexicon. Expert Syst Appl 116:285–298

    Article  Google Scholar 

  7. Oliveira N, Cortez P, Areal N (2016) Stock market sentiment lexicon acquisition using microblogging data and statistical measures. Decis Support Syst 85:62–73

    Article  Google Scholar 

  8. Viegas F, Alvim MS, Canuto S, Rosa T, Gonçalves MA, Rocha L (2020) Exploiting semantic relationships for unsupervised expansion of sentiment lexicons. Inf Syst 94:101606

    Article  Google Scholar 

  9. Wu F, Huang Y, Song Y, Liu S (2016) Towards building a high-quality microblog-specific Chinese sentiment lexicon. Decis Support Syst 87:39–49

    Article  Google Scholar 

  10. Allison B, Guthrie D, Guthrie L (2006) Another look at the data sparsity problem. In: International conference on text, speech and dialogue, pp 327–334. Springer, Berlin

  11. Mei LL, Huang HY, Zhou XY, Mao XL (2016) A survey on sentiment Lexicon construction. J Chin Inf Process 30(5):19–27

    Google Scholar 

  12. Blanke T, Bryant M, Hedges M (2020) Understanding memories of the Holocaust—a new approach to neural networks in the digital humanities. Digital Scholarsh Hum 35(1):17–33

    Article  Google Scholar 

  13. Tao Y (2019) Exploring the development and application of readers’ knowledge from the “Readers’ Salon”. Libraly J 38(12):67–71

    Google Scholar 

  14. Xing FZ, Pallucchini F, Cambria E (2019) Cognitive-inspired domain adaptation of sentiment lexicons. Inf Process Manag 56(3):554–564

    Article  Google Scholar 

  15. Kamps J, Marx M, Mokken RJ, De Rijke M (2004) Using WordNet to measure semantic orientations of adjectives. In: LREC, Vol 4, pp 1115–1118

  16. Esuli A, Sebastiani F (2007) Pageranking wordnet synsets: an application to opinion mining. In: Proceedings of the 45th annual meeting of the association of computational linguistics, pp 424–431

  17. Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. Comput Linguist 37(1):9–27

    Article  Google Scholar 

  18. Khan FH, Qamar U, Bashir S (2016) Senti-CS: building a lexical resource for sentiment analysis using subjective feature selection and normalized Chi-Square-based feature weight generation. Expert Syst 33(5):489–500

    Article  Google Scholar 

  19. Wang K, Xia R (2016) A survey on automatical construction methods of sentiment lexicons. Acta Automatica Sinica 42(4):495–511

    Google Scholar 

  20. Tang F, Fu L, Yao B, Xu W (2019) Aspect based fine-grained sentiment analysis for online reviews. Inf Sci 488:190–204

    Article  Google Scholar 

  21. Poria S, Cambria E, Gelbukh A (2016) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl-Based Syst 108:42–49

    Article  Google Scholar 

  22. Yang H, Zeng B, Yang J, Song Y, Xu R (2021) A multi-task learning model for chinese-oriented aspect polarity classification and aspect term extraction. Neurocomputing 419:344–356

    Article  Google Scholar 

  23. 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 

  24. Chen Y, Zhou C, Li T, Wu H, Zhao X, Ye K, Liao J (2019) Named entity recognition from Chinese adverse drug event reports with lexical feature based BiLSTM-CRF and tri-training. J Biomed Inform 96:103252

    Article  Google Scholar 

  25. Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

  26. Zhang Q, Sun Y, Zhang L, Jiao Y, Tian Y (2021) Named entity recognition method in health preserving field based on BERT. Procedia Comput Sci 183:212–220

    Article  Google Scholar 

  27. Labusch K, Kulturbesitz P, Neudecker C, Zellhöfer D (2019) BERT for named entity recognition in contemporary and historical German. In: Proceedings of the 15th conference on natural language processing, Erlangen, Germany, pp 8–11

  28. Tabassum J, Maddela M, Xu W, Ritter A (2020) Code and named entity recognition in stackoverflow. arXiv preprint arXiv:2005.01634

  29. Cambria E, Liu Q, Decherchi S, Xing F, Kwok K (2022) SenticNet 7: a commonsense-based neurosymbolic AI framework for explainable sentiment analysis. In: Proceedings of LREC 2022

  30. Chen J, Chen Y, He Y, Xu Y, Zhao S, Zhang Y (2022) A classified feature representation three-way decision model for sentiment analysis. Appl Intell 52(7):7995–8007

    Article  Google Scholar 

  31. Huang Q, Zhou C, Wu J, Liu L, Wang B (2020) Deep spatial–temporal structure learning for rumor detection on Twitter. Neural Comput Appl 2:1–11

    Google Scholar 

  32. Li W, Shao W, Ji S, Cambria E (2022) BiERU: bidirectional emotional recurrent unit for conversational sentiment analysis. Neurocomputing 467:73–82

    Article  Google Scholar 

  33. Tanaka H, Shinnou H, Cao R, Bai J, Ma W (2019) Document classification by word embeddings of Bert. In: International conference of the Pacific association for computational linguistics, pp 145–154. Springer, Singapore

  34. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  35. Xie Z, Wang SI, Li J, Lévy D, Nie A, Jurafsky D, Ng AY (2017) Data noising as smoothing in neural network language models. arXiv preprint arXiv:1703.02573

  36. Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification. Adv Neural Inf Process Syst 28:649–657

    Google Scholar 

  37. Wei J, Zou K (2019) Eda: easy data augmentation techniques for boosting performance on text classification tasks. arXiv preprint arXiv:1901.11196

  38. Yu AW, Dohan D, Luong MT, Zhao R, Chen K, Norouzi M, Le QV (2018) Qanet: combining local convolution with global self-attention for reading comprehension. arXiv preprint arXiv:1804.09541

  39. Hu J, Cen Y, Wu C (2018) Constructing sentiment dictionary with deep learning: case study of financial data. Data Anal Knowl Discov 2(10):95–102

    Google Scholar 

  40. Cho H, Kim S, Lee J, Lee JS (2014) Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews. Knowl-Based Syst 71:61–71

    Article  Google Scholar 

  41. Ekman P (1992) An argument for basic emotions. Cogn Emot 6(3–4):169–200

    Article  Google Scholar 

  42. Liu Z (2018) A study of Chinese poetry generation based on deep learning (Doctoral dissertation, Changsha: Hunan Normal University)

  43. Wang X, Wang Z (2020) Question answering system based on diease knowledge base. In: 2020 IEEE 11th international conference on software engineering and service science (ICSESS), pp 351–354. IEEE

  44. Yang J, Fang Y (1998) Automatic recognition of handwritten Chinese text based on linguistic knowledge. J Comput Res Dev 35(7):93–97

    Google Scholar 

  45. Reimers N, Gurevych I (2019) Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084

  46. Brandsen A, Verberne S, Lambers K, Wansleeben M, Calzolari (2020) Creating a dataset for named entity recognition in the archaeology domain. In: Conference proceedings LREC 2020, pp 4573–4577. The European Language Resources Association

  47. Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10(7):1895–1923

    Article  Google Scholar 

  48. Sprugnoli R, Tonelli S, Marchetti A, Moretti G (2016) Towards sentiment analysis for historical texts. Digital Scholarsh Hum 31(4):762–772

    Article  Google Scholar 

  49. Moreno-Ortiz A (2017) Lingmotif: sentiment analysis for the digital humanities. In: Proceedings of the software demonstrations of the 15th conference of the European chapter of the association for computational linguistics, pp 73–76

  50. Hou, Y., & Frank, A. (2015). Analyzing sentiment in classical Chinese poetry. In Proceedings of the 9th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH) (pp. 15–24).

  51. Wei J, Liao J, Yang Z, Wang S, Zhao Q (2020) BiLSTM with multi-polarity orthogonal attention for implicit sentiment analysis. Neurocomputing 383:165–173

    Article  Google Scholar 

  52. Wang, B., Hu, R., & Yang, L. (2019, October). Constructing the image graph of tang poetry. In CCF International Conference on Natural Language Processing and Chinese Computing (pp. 426–434). Springer, Cham.

  53. Shen, Y., Ma, Y., Li, C., Li, S., & Gu, M. (2019, May). Sentiment Analysis for Tang Poetry Based on Imagery Aided and Classifier Fusion. In International Conference on Artificial Intelligence for Communications and Networks (pp. 283–290). Springer, Cham.

  54. Nguyen CV, Le KH, Tran AM, Pham QH, Nguyen BT (2022) Learning for amalgamation: a multi-source transfer learning framework for sentiment classification. Inform Sci 2:8856

    Google Scholar 

  55. Li H, Chen Q, Zhong Z, Gong R, Han G (2022) E-word of mouth sentiment analysis for user behavior studies. Inf Process Manag 59(1):102784

    Article  Google Scholar 

  56. Ray B, Garain A, Sarkar R (2021) An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews. Appl Soft Comput 98:106935

    Article  Google Scholar 

  57. Pasupa K, Ayutthaya SN, T. (2022) Hybrid deep learning models for Thai sentiment analysis. Cogn Comput 14(1):167–193

    Article  Google Scholar 

  58. Lu Y, Castellanos M, Dayal U, Zhai C (2011) Automatic construction of a context-aware sentiment lexicon: an optimization approach. In: Proceedings of the 20th international conference on World wide web, pp 347–356

  59. Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 31(2):102–107

    Article  Google Scholar 

  60. Li X, Zhang H, Zhou XH (2020) Chinese clinical named entity recognition with variant neural structures based on BERT methods. J Biomed Inform 107:103422

    Article  Google Scholar 

  61. Wu S, Song X, Feng Z (2021) MECT: multi-metadata embedding based cross-transformer for Chinese named entity recognition. arXiv preprint arXiv:2107.05418

  62. Wang Z, Ho SB, Cambria E (2020) Multi-level fine-scaled sentiment sensing with ambivalence handling. Int J Uncertain Fuzziness Knowl-Based Syst 28(04):683–697

    Article  Google Scholar 

Download references

Acknowledgements

This paper was supported by the National Natural Science Foundation of China [72074108], the Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX21_0026], the National Research Foundation of Korea [2022R1A2B5B02002359], the Fundamental Research Funds for the Central Universities [010814370113], as well as the program of the China Scholarship Council (award to Wei Zhang for 1 year’s study abroad at the Yonsei University). We would like to acknowledge the annotator, Tao Fan, a doctoral student in Information Science from Nanjing University, as well as his valuable suggestions. We would also like to express our special thanks to the editor and reviewers for their very constructive comments and suggestions.

Author information

Authors and Affiliations

  1. School of Information Management, Nanjing University, Nanjing, 210023, China

    Wei Zhang, Hao Wang & Sanhong Deng

  2. Department of Library and Information Science, Yonsei University, Seoul, 03722, Republic of Korea

    Wei Zhang & Min Song

  3. Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing, 210023, China

    Wei Zhang, Hao Wang & Sanhong Deng

Authors
  1. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanhong Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization and Software and Writing-Original draft, WZ and HW; Formal analysis and Validation and Supervision, MS and SD. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Hao Wang.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, W., Wang, H., Song, M. et al. A method of constructing a fine-grained sentiment lexicon for the humanities computing of classical chinese poetry. Neural Comput & Applic 35, 2325–2346 (2023). https://doi.org/10.1007/s00521-022-07690-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07690-8

Keywords