@inproceedings{phuc-etal-2026-edupulse,
title = "{E}du{P}ulse: A Practical {LLM}-Enhanced Opinion Mining System for {V}ietnamese Student Feedback in Educational Platforms",
author = "Phuc, Nguyen Xuan and
Xuan, Phi Nguyen and
Nguyen, Vinh-Tiep and
Van, Th{\`i}n Dang and
Nguyen, Ngan Luu-Thuy",
editor = {Matusevych, Yevgen and
Eryi{\u{g}}it, G{\"u}l{\c{s}}en and
Aletras, Nikolaos},
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-industry.25/",
pages = "338--365",
ISBN = "979-8-89176-384-5",
abstract = "Opinion mining from real-world student feedback presents significant practical challenges, such as handling linguistic noise (slang, teencode) and the need for scalable and maintainable systems, which are often overlooked in academic research. This paper introduces EduPulse, a practical opinion mining system designed specifically to analyze student feedback in Vietnamese. Our application performs four opinion analysis tasks, including Sentiment Classification, Category-based Sentiment Classification, Suggestion Detection, and Opinion Summarization. We design the hybrid architecture that strategically balances performance, cost, and maintainability. This architecture leverages the robustness of Large Language Models (LLMs) for complex, noise-sensitive tasks as sentiment classification and suggestion detection, while employing a specialized, lightweight neural model for high-throughput, low-cost solutions. Our experiments show that applying the LLM-based approach achieves high robustness, justifying its operational cost by eliminating expensive retraining cycles. Furthermore, we demonstrate that our collaborative modular architecture significantly improves task performance (+7.6{\%}) compared to traditional approaches, offering a practical design for industry-focused Natural Language Processing applications."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="phuc-etal-2026-edupulse">
<titleInfo>
<title>EduPulse: A Practical LLM-Enhanced Opinion Mining System for Vietnamese Student Feedback in Educational Platforms</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nguyen</namePart>
<namePart type="given">Xuan</namePart>
<namePart type="family">Phuc</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Phi</namePart>
<namePart type="given">Nguyen</namePart>
<namePart type="family">Xuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vinh-Tiep</namePart>
<namePart type="family">Nguyen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thìn</namePart>
<namePart type="given">Dang</namePart>
<namePart type="family">Van</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ngan</namePart>
<namePart type="given">Luu-Thuy</namePart>
<namePart type="family">Nguyen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yevgen</namePart>
<namePart type="family">Matusevych</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gülşen</namePart>
<namePart type="family">Eryiğit</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nikolaos</namePart>
<namePart type="family">Aletras</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Rabat, Morocco</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-384-5</identifier>
</relatedItem>
<abstract>Opinion mining from real-world student feedback presents significant practical challenges, such as handling linguistic noise (slang, teencode) and the need for scalable and maintainable systems, which are often overlooked in academic research. This paper introduces EduPulse, a practical opinion mining system designed specifically to analyze student feedback in Vietnamese. Our application performs four opinion analysis tasks, including Sentiment Classification, Category-based Sentiment Classification, Suggestion Detection, and Opinion Summarization. We design the hybrid architecture that strategically balances performance, cost, and maintainability. This architecture leverages the robustness of Large Language Models (LLMs) for complex, noise-sensitive tasks as sentiment classification and suggestion detection, while employing a specialized, lightweight neural model for high-throughput, low-cost solutions. Our experiments show that applying the LLM-based approach achieves high robustness, justifying its operational cost by eliminating expensive retraining cycles. Furthermore, we demonstrate that our collaborative modular architecture significantly improves task performance (+7.6%) compared to traditional approaches, offering a practical design for industry-focused Natural Language Processing applications.</abstract>
<identifier type="citekey">phuc-etal-2026-edupulse</identifier>
<location>
<url>https://aclanthology.org/2026.eacl-industry.25/</url>
</location>
<part>
<date>2026-03</date>
<extent unit="page">
<start>338</start>
<end>365</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T EduPulse: A Practical LLM-Enhanced Opinion Mining System for Vietnamese Student Feedback in Educational Platforms
%A Phuc, Nguyen Xuan
%A Xuan, Phi Nguyen
%A Nguyen, Vinh-Tiep
%A Van, Thìn Dang
%A Nguyen, Ngan Luu-Thuy
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F phuc-etal-2026-edupulse
%X Opinion mining from real-world student feedback presents significant practical challenges, such as handling linguistic noise (slang, teencode) and the need for scalable and maintainable systems, which are often overlooked in academic research. This paper introduces EduPulse, a practical opinion mining system designed specifically to analyze student feedback in Vietnamese. Our application performs four opinion analysis tasks, including Sentiment Classification, Category-based Sentiment Classification, Suggestion Detection, and Opinion Summarization. We design the hybrid architecture that strategically balances performance, cost, and maintainability. This architecture leverages the robustness of Large Language Models (LLMs) for complex, noise-sensitive tasks as sentiment classification and suggestion detection, while employing a specialized, lightweight neural model for high-throughput, low-cost solutions. Our experiments show that applying the LLM-based approach achieves high robustness, justifying its operational cost by eliminating expensive retraining cycles. Furthermore, we demonstrate that our collaborative modular architecture significantly improves task performance (+7.6%) compared to traditional approaches, offering a practical design for industry-focused Natural Language Processing applications.
%U https://aclanthology.org/2026.eacl-industry.25/
%P 338-365
Markdown (Informal)
[EduPulse: A Practical LLM-Enhanced Opinion Mining System for Vietnamese Student Feedback in Educational Platforms](https://aclanthology.org/2026.eacl-industry.25/) (Phuc et al., EACL 2026)
ACL