BERTOPIC FOR ACCELERATED GOVERNMENT AID

Abstract

ABSTRACT OF THE INVENTION: To develop a composable application that leverages BErTopic [Bidirectional Encoder Representations from Transformers] modeling technique to analyze and provide meaningful insights from aid requests and thereby providing quick, and efficient help to 10 humanitarian missions. Our application hopes to bridge the gap between people's needs and actual help provided by making people's opinions as a factor in government's crisis management response. We were able to solve a lot of issues identified in the existing research related to topic modeling and refugee need analysis. The application is easy to use and performant enough to derive insights frorn a lot of different sources while 15 maintaining its core, modular and agnostic to outside platforms. We can be sure that the application would receive updates to its functionality and will soon be looking at even better ways to analyze topics from the dataset.

Specification

DESCRIPTION:
[0001] This topic is designed to develop a web-based application that utilizes advanced
Natural Language Processing (NLP) techniques, particularly BERTopic, to support
5 government and humanitarian organizations in streamlining aid response and resource
allocation. BERTopic, a model based on Bidirectional Encoder Representations from
Transformers (BERT) for topic modeling, allows for nuanced extraction of themes from
large volumes of unstructured text. By applying this model, the platform will analyze,
classify, and visualize aid-related requests from citizens and communities, helping
1 o authorities prioritize and respond quickly and effectively to urgent needs. The primary goal
of this project is to automate the categorization of aid requests, helping government bodies
quickly identify urgent cases across various themes, such as medical assistance, disaster
relief, and basic necessities. This enables more efficient and targeted resource allocation,
allowing agencies to recognize and respond to high-demand needs, ultimately improving
15 decision-making. Additionally, the platform will deliver real-time insights into areas with
pressing aid requirements, empowering authorities to take proactive, data-driven actions.
To provide transparency and usability, the system incorporates interactive dashboards and
visualizations that display insights, trends, and urgency levels of requests. Summary
statistics and metrics will track factors like response times and common issues, crucial for
20 effective reporting and planning. Spam filtering ensures that only genuine aid requests are
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processed, reducing false positives and optimizing resources. Additionally, the feedback
mechanism allows government officials to mark certain requests as high priority,
continuously refining the model's ability to detect critical cases.
PRIOR ART AND BACKGROUND:
[0002] The development of "BERTopic for Accelerated Government Aid" builds upon
existing technologies and methodologies in topic modeling, natural language
30 processing, and automated aid response systems. Traditional approaches to
handling large volumes of aid requests often involve manual sorting and
prioritization, which is labor-intensive and prone to delays, particularly in urgent
situations. Over time, however, advancements in NLP have led to more efficient
automated solutions.
[0003] One of the significant foundations for this project is the use of topic
modeling-a technique that has been applied in various domains to uncover hidden
5 structures within large text datasets. Classical models such as Latent Dirichlet
Allocation (LDA) and Latent Semantic Analysis (LSA) were initially employed to
categorize and summarize vast collections of documents. These models, however,
typically require high computational resources and struggle with processing diverse
language structures, often limiting their accuracy and adaptability in real-time
1 o applications.
[0004] Hierarchical Dirichlet Process (HOP) is another classical approach that
inspired more recent topic modeling frameworks. An example of this in the domain
of aid response is Refinery, an NLP-based web application built to simplify the use
15 of complex NLP tools. While low in resource requirements, Refinery has limitations
in terms of flexibility, as it is not easily deployable online nor integrable with modern
databases. Furthermore, HOP and similar methods lack the dynamic updating
capabilities that are crucial for handling the rapidly evolving language of citizen
requests. A more recent advancement came with the use of Bidirectional Encoder
20 Representations from Transformers (BERT) for topic modeling and classification,
which introduced a new standard for accuracy in NLP tasks. BERT-based models
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excel in understanding context and have outperformed earlier algorithms in tasks
involving nuanced language, such as classification, clustering, and translation.
[0005] The Disaster Tweets Classification project, which uses BERT and Linear
Support Vector Classifier (SVC) models, is a notable example. This project classifies
tweets during disaster events to aid relief efforts, achieving about 82% accuracy in
identifying disaster-related content. However, it relies on pre-mined Twitter data,
making it static and less adaptable to real-time applications. BERTopic combines
the strengths of BERT's context-aware embeddings with hierarchical topic
clustering, creating a model capable of dynamically updating topics and generating
human-interpretable insights. BERTopic's ability to categorize unstructured text data
is especially advantageous in humanitarian contexts, where diverse expressions of
aid requests need to be classified into actionable insights. It provides an adaptable
and explainable niodel that outperforms earlier approaches in accuracy and
interpretability.
[0006] The proposed platform will extend the capabilities of BERTopic to develop a
5 deployable system that not only provides highly accurate topic modeling for government
aid applications but also integrates seamlessly with various data sources and existing
government databases. The platform's composable architecture is inspired by the need for
flexibility and adaptability in government technology, addressing the shortcomings of prior
static systems and enabling modular expansion. Furthermore, by integrating visualization
10 libraries and interactive dashboards, it will offer an accessible and interpretable interface
for decision-makers, who can prioritize requests, filter out spam, and adapt quickly to
emerging aid needs based on real-time insights. This background and prior art establish
the technical foundation and highlight the novel contributions of "BERTopic for Accelerated
Government Aid," positioning it as a robust solution designed to streamline and improve
15 government response to urgent humanitarian needs.
C> OBJECTIVE:
20 [0007] To provide a portable/deployable software that can be installed on any machine
which can then be used to analyze and provide insights on multitude of requests received
by the government during events of disaster/need or they can also use it to· improve any
of their currently provided services by collecting feedback and then analyzing it with the
software. To feed these requests in text through the pipelines laid down in the application
25 - we also strive to provide composable interfaces that technical people could implement
on their own to effectively gather these requests - like some organizations might want to
take their requests through calls- which can be done through any speech-to-text software
CIO 30
that is available today and then send it down the pipelines that the software uses in the
form of text. These interfa·ces could also be implemented as a way to gather requests from
a website or be made into bats that can be used to do social media listening -that is,
passively gathering requests etc. The possibilities are endless and this would greatly
reduce the time/money that would be required to provide help and organizations could then
use the funds that were saved to other resources that are needed, thereby leading to
greater effort towards humanitarian missions.
SUMMARY:
(0008] The "BERTopic for Accelerated Government Aid" project aims to create a webs
based application that uses BERTopic, a powerful NLP model, to analyze and categorize
citizen aid requests for efficient government response. By automating the grouping and
prioritization of requests across themes like disaster relief and medical assistance, the
platform will help government agencies quickly identify urgent cases and allocate
resources effectively. Its adaptable, composable architecture allows easy integration with
I 0 existing data sources, while real-time visualizations and interactive dashboards provide
clear, actionable insights. Ultimately, this system is designed to enhance productivity,
accelerate aid response, and improve decision-making for humanitarian needs.
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DETAILED TECHNICAL DESCRIPTION:
[0009] This project is on battle tested frameworks, libraries and tools to make this
software reliable and efficient. Clients are the primary stakeholders that the
organization that has deployed this application would like to cater to, such clients
can be refugees, business partners or any people in need. End user here could be
20 a gqvernment entity or a non-governmental organization or a company that wants
to understand their primary stakeholders in order to better serve them. They
configure, alter and deploy the software to in-order to analyze data. The end users
can control each and every operation involved in the process through in-browser
web user interface, different roles can be given to different users, thereby restricting
25 access to critical operations and data. Docker provides a way to package the
software and use it anywhere. It works by creating a self-contained environment
called container that is isolated from the OS and can be run without having to install
any additional dependencies as the blueprint (a.k.a image) of the container takes
care of this step. All the user has to do is build the container from the image and
30 then run it. This is particularly beneficial in humanitarian aid settings, where technical
infrastructure might vary greatly. Additionally, Docker facilitates easy deployment on
. cloud platforms, allowing organizations to quickly scale their resources based on .....
~ demand. BERTopic functionality is written as a submodule within Flask to ensure
~ greater control over when and how the process gets executed. VectorDb will be
N > 35 using an all-performant Relational Database called PostgreSQL with an extension
called vector which is used for vector similarity search. Frontend The frontend is
made possible by a frontend framework called Angular and a visualization library
called 03 is and finally Bootstrap for styling.
5 BRIEF DESCRIPTION OF THE DRAWING:
[0010] This architecture diagram represents a topic modeling and request aggregation
system that uses BERTopic, hosted on a Flask backend. At the top, an aggregator module
collects requests from various channels, such as emails, text messages, and audio calls,
10 standardizing them into text format before forwarding them to the core system. The Flask
application acts as the main framework, managing incoming requests and facilitating data
flow across componHniH
Fig 1. Represents Architecture Diagram
Fig 2. Prototype Implementation
[0011] The heart ofth~ application is the BERTopic module, which performs topic modeling
on these requests. Within BERTopic, several processes are at work: embeddings
transform the text into numerical representations, dimensionality reduction simplifies these
embeddings foreasier processing, and c-TF-IDF calculates term relevance to create topic-
20 specific keywords. HDBSCAN, a clustering algorithm, identifies groups of similar requests
within the embeddings, while the final representation module structures the output into
distinct, interpretable topics. The!'e embeddings are stored in a Ve'c torDB, a vector
database that allows efficient retrieval and similarity matching, enabling updates as new
requesis arrive. The entire application is encapsulated within a Docker container for easy
~5 deployment, scalability, and consistency across environments. The user interface,
developed using Angular, 03.js, and Bootstrap, allows end users to interact with the
system, visualizing topics and accessing categorized information. Angular ensures a
dynamic and interactive experience, D3.js provides data visualizations, and Bootstrap
makes the interface responsive and visually appealing. End users at the bottom of the
.... 30 diagram access these insights through the frontend, which presents complex data in an
easily interpretable form.

CLAIM:
We claim
I. Increased productivity - requests are grouped based on commonality and thereby
helping large number of people at the same time. The loT-based monitoring system
as claimed in claim 1, will make the server management easy for everyone, so they
10 can supervise them using their phone.
2. Provides immediate and quick help if request is found to be similar to any previously
discovered label.
3. Composability of the application helps organization to customize parts that are
specific to their needs over having to implement everything all over again.
4. Multi platform - doesnt need to be built specific to each os
5. GUI based config alterations to enable quick revisions of topics generated

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