Abstract
Legal information retrieval holds a significant importance to lawyers and legal professionals. Its significance has grown as a result of the vast and rapidly increasing amount of legal documents available via electronic means. Legal documents, which can be considered flat file databases, contain information that can be used in a variety of ways, including arguments, counter-arguments, justifications, and evidence. As a result, developing automated mechanisms for extracting important information from legal opinion texts can be regarded as an important step toward introducing artificial intelligence into the legal domain. Identifying advantageous or disadvantageous statements within these texts in relation to legal parties can be considered as a critical and time consuming task. This task is further complicated by the relevance of context in automatic legal information extraction. In this paper, we introduce a solution to predict sentiment value of sentences in legal documents in relation to its legal parties. The Proposed approach employs a fine-grained sentiment analysis (Aspect-Based Sentiment Analysis) technique to achieve this task. Sigmalaw PBSA is a novel deep learning-based model for ABSA which is specifically designed for legal opinion texts. We evaluate the Sigmalaw PBSA model and existing ABSA models on the SigmaLaw-ABSA dataset which consists of 2000 legal opinion texts fetched from a public online data base. Experiments show that our model outperforms the state-of-the-art models. We also conduct an ablation study to identify which methods are most effective for legal texts.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Legal-BERT model - https://osf.io/s8dj6/.
- 2.
Spacy Toolkit - https://spacy.io/.
References
Rajapaksha, I., Mudalige, C.R., Karunarathna, D., de Silva, N., Rathnayaka, G., Perera, A.S.: Rule-based approach for party-based sentiment analysis in legal opinion texts. In: 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer)
Mudalige, C.R., et al.: SigmaLaw-ABSA: dataset for aspect-based sentiment analysis in legal opinion texts. In: 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS). IEEE (2020)
Samarawickrama, C., de Almeida, M., de Silva, N., Ratnayaka, G., Perera, A.S.: Party identification of legal documents using co-reference resolution and named entity recognition. In: 2020 IEEE 15th International Conference on Industrial and Information Systems (2020)
de Almeida, M., Samarawickrama, C., de Silva, N., Ratnayaka, G., Perera, A.S.: Legal party extraction from legal opinion text with sequence to sequence learning. In: 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer) (2020)
Moralwar, S., Deshmukh, S.: Different approaches of sentiment analysis. Int. J. Comput. Sci. Eng. 3(3), 160–165 (2015)
Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2015)
Bhoi, A., Joshi, S.: Various approaches to aspect-based sentiment analysis. ArXiv, abs/1805.01984 (2018)
Pontiki, M., Galanis, D., Papageorgiou, H., et al.: Semeval-2016 task 5: aspect based sentiment analysis, pp. 19–30, January 2016
Sugathadasa, K., et al.: Synergistic union of word2vec and lexicon for domain specific semantic similarity. In: IEEE International Conference on Industrial and Information Systems (ICIIS), pp. 1–6 (2017)
Lee v. United States, in US, vol. 432, no. 76-5187, p. 23, Supreme Court (1977)
Gamage, V., Warushavithana, M., de Silva, N., Perera, A.S., Ratnayaka, G., Rupasinghe, T.: Fast Approach to build an automatic sentiment annotator for legal domain using transfer learning. In: Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (2018)
Ratnayaka, G., Rupasinghe, T., de Silva, N., Gamage, V., Warushavithana, M., Perera, A.S.: Shift-of-perspective identification within legal cases. In: Proceedings of the 3rd Workshop on Automated Detection, Extraction and Analysis of Semantic Information in Legal Texts (2019)
Piryani, R., Gupta, V., Singh, V.K., Ghose, U.: A linguistic rule-based approach for aspect-level sentiment analysis of movie reviews. In: Bhatia, S.K., Mishra, K.K., Tiwari, S., Singh, V.K. (eds.) Advances in Computer and Computational Sciences. AISC, vol. 553, pp. 201–209. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-3770-2_19
Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. arXiv preprint arXiv:1512.01100 (2015)
Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (2016)
Cheng, J., Zhao, S., Zhang, J., King, I., Zhang, X., Wang, H.: Aspect-level sentiment classification with heat (hierarchical attention) network. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 97–106 (2017)
Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on EMNLP (2017)
Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893 (2017)
Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv preprint arXiv:1909.03477 (2019)
Zhao, P., Hou, L., Wu, O.: Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification. Knowl. Based Syst. 193, 105443 (2020)
Zhang, Y., Liu, Q., Song, L.: Sentence-state LSTM for text representation. In: ACL (2018)
Demotte, P., Senevirathne, L., Karunanayake, B., Munasinghe, U., Ranathunga, S.: Sentiment analysis of Sinhala news comments using sentence-state LSTM networks. In: Moratuwa Engineering Research Conference (MERCon) 2020, pp. 283–288 (2020)
He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: Effective attention modeling for aspect-level sentiment classification. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 1121–1131 (2018)
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Association for Computational Linguistics, pp. 1532–1543, October 2014
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: ACL, pp. 4171–4186, June 2019
Ratnayaka, G., de Silva, N., Perera, A.S., Pathirana, R.: Effective approach to develop a sentiment annotator for legal domain in a low resource setting. arXiv preprint arXiv:2011.00318 (2020)
Gu, S., Zhang, L., Hou, Y., Song, Y.: A position-aware bidirectional attention network for aspect-level sentiment analysis. In: Proceedings of the 27th International Conference on Computational Linguistics. ACL, August 2018
Liu, Q., Zhang, H., Zeng, Y., Huang, Z., Wu, Z.: Content attention model for aspect based sentiment analysis. In: Proceedings of the 2018 World Wide Web Conference (2018)
Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900 (2016)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Rajapaksha, I., Mudalige, C.R., Karunarathna, D., de Silva, N., Perera, A.S., Ratnayaka, G. (2021). Sigmalaw PBSA - A Deep Learning Model for Aspect-Based Sentiment Analysis for the Legal Domain. In: Strauss, C., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2021. Lecture Notes in Computer Science(), vol 12923. Springer, Cham. https://doi.org/10.1007/978-3-030-86472-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-86472-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86471-2
Online ISBN: 978-3-030-86472-9
eBook Packages: Computer ScienceComputer Science (R0)