 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: March 2026
Volume 11 | Issue 3
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21686
Article Published : 8549
Total Authors : 22487
Total Reviewer : 811
Total Countries : 159
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Enhancing Tenant-Owner Communication via BERT-Based Severity Prediction of Housing Complaints |
| Authors Name: | Laila k , Viswanathan V , Senoj S , Stanley A |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_203492
|
| Published Paper Id: | IJRTI2505232 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | Housing maintenance complaints vary in severity, making it crucial to prioritize them ef effectively for timely resolution. Property owners managing many tenants often struggle to monitor and address these complaints efficiently. This paper presents a BERT-based severity prediction model to automate the classification of housing complaints, enabling property managers to handle issues more effectively. The model is trained on a dataset of 311 NYC (New York City) service requests, utilizing transformer-based NLP techniques to assess and categorize complaint severity. Our approach achieves high accuracy in automated complaint triaging, significantly outperforming traditional methods. Our model uses MobileBERT and achieves 91.37% accuracy in predicting the severity of complaints. The integrated application allows property owners to efficiently track, prioritize, and resolve issues, reducing response times and enhancing operational efficiency. The future development includes model optimization alongside diverse dataset expansion and user experience enhancement to enable practical deployment of the application. |
| Keywords: | Housing maintenance complaints, Severity prediction, BERT-based model, Natural Language Processing (NLP), Transformer model, Automated complaint triaging, Property management, Tenant-owner communication, Complaint classification, Issue prioritization. |
| Cite Article: | "Enhancing Tenant-Owner Communication via BERT-Based Severity Prediction of Housing Complaints", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c298-c308, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505232.pdf |
| Downloads: | 000442 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2505232
Registration ID:203492
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: c298-c308 Country: Chennai, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505232 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505232 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||