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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM67745

TermsBuster: An AI-Powered Smart Privacy Policy and Legal Document Analysis Assistant

Stream/Area:

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Paper ID:

IJSREM67745

Volume:

10 |

10 |

Oct 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

S

S.SHOBANA

DEPARTMENT OF COMPUTER APPLICATIONS

VELLALAR COLLEGE FOR WOMEN

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Mentors

D

Dr.P.Anitha

DEPARTMENT OF COMPUTER APPLICATIONS

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Abstract

Legal, privacy, and policy documents contain important information about obligations, permissions, restrictions, conditions, and user rights. However, these documents are often lengthy and written using complex terminology, making them difficult for ordinary users to understand. TermsBuster is an AI-powered smart document analysis assistant developed to provide a first-level analysis of privacy policies and other legal documents. The system allows users to upload supported documents and processes their textual content using document extraction and natural language processing techniques. The proposed system performs document classification, text preprocessing, summarization, important-term identification, and rule-based risk analysis. TF-IDF-based text analysis is used to identify informative terms, while predefined rules, keywords, sentence-level analysis, negation handling, and safe-phrase information are used to support risk indication. The system supports different categories of legal and privacy-related documents, including contracts and agreements, legal notices, privacy policies, financial documents, ownership documents, intellectual property documents, criminal offense documents, regulatory compliance documents, employment documents, and court judgments. The results are presented through an interactive Streamlit interface. TermsBuster is designed as an informational assistant and does not replace professional legal advice. The proposed system provides a convenient approach for helping users understand complex legal and privacy-related documents before detailed review.

KEYWORDS

Legal Document Analysis Privacy Policy Natural Language Processing Text Classification TF-IDF Risk Analysis

Cite This Article

S.SHOBANA, Dr.P.Anitha, "TermsBuster: An AI-Powered Smart Privacy Policy and Legal Document Analysis Assistant," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-7, Oct 2026.

Full Article/Conclusion

TermsBuster is an AI-powered smart privacy policy and legal document analysis assistant developed to make complex legal and privacy-related documents easier to understand at a first level. The system integrates document upload, text extraction, preprocessing, document classification, summarization, TF-IDF-based important-term analysis, and rule-based risk indication within a single Streamlit application.The proposed system reduces the effort required to obtain an initial overview of a lengthy document. Instead of manually searching through a document for its category, important terms, and potentially significant statements, the user can submit the document and receive structured information through the application. The use of TF-IDF provides a method for identifying informative terms, while rule-based analysis provides an understandable approach for highlighting potentially important content.The classification module further improves the organization of the analysis by identifying the general type of document.

References

[1] M. Bokaie Hosseini, P. K. C, I. Reyes, and S. Egelman, “Identifying and Classifying Third-party Entities in Natural Language Privacy Policies,” Proceedings of the Second Workshop on Privacy in NLP, pp. 18–27, 2020. [2] I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras, and I. Androutsopoulos, “LEGAL-BERT: The Muppets Straight Out of Law School,” Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 2898–2904, 2020. [3] D. Mamakas, P. Tsotsi, I. Androutsopoulos, and I. Chalkidis, “Processing Long Legal Documents with Pre-trained Transformers: Modding LegalBERT and Longformer,” Proceedings of the Natural Legal Language Processing Workshop 2022, 2022. [4] N. Limsopatham, “Effectively Leveraging BERT for Legal Document Classification,” Proceedings of the Natural Legal Language Processing Workshop 2021, pp. 210–216, 2021. [5] M. Masala, R. C. A. Iacob, A. S. Uban, M. Cidota, H. Velicu, T. Rebedea, and M. Popescu, “jurBERT: A Romanian BERT Model for Legal Judgement Prediction,” Proceedings of the Natural Legal Language Processing Workshop 2021, pp. 86–94, 2021. [6] Y. Shvartzshanider, A. Balashankar, T. Wies, and L. Subramanian, “Beyond the Text: Analysis of Privacy Statements through Syntactic and Semantic Role Labeling,” Proceedings of the Natural Legal Language Processing Workshop 2023, pp. 85–98, 2023. [7] A. Shukla, P. Bhattacharya, S. Poddar, R. Mukherjee, K. Ghosh, P. Goyal, and S. Ghosh, “Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation,” Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, pp. 1048–1064, 2022. [8] B. Sun and X. Zhou, “ignore at SemEval-2024 Task 5: A Legal Classification Model with Summary Generation and Contrastive Learning,” Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pp. 530–535, 2024. [9] E. Lukose, S. De, and J. Johnson, “Privacy Pitfalls of Online Service Terms and Conditions: A Hybrid Approach for Classification and Summarization,” Proceedings of the Natural Legal Language Processing Workshop 2022, pp. 65–75, 2022. [10] This list has a problem: strict alphabetical order would require E. Lukose before I. Chalkidis, so let me give you the genuinely correct final sequence below.  

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Publication Date

10/9/2026

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Double-Blind Peer Review

Review Rounds: 2

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