Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67745
Volume:
10 |
10 |
Oct 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
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.
DEPARTMENT OF COMPUTER APPLICATIONS
VELLALAR COLLEGE FOR WOMEN
DEPARTMENT OF COMPUTER APPLICATIONS
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
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.
[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.
Publication Date
10/9/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
8
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...