Robust Complaint Processing in Portuguese

The Natural Language Processing (NLP) community has witnessed huge improvements in the last years. However, most achievements are evaluated on benchmarked curated corpora, with little attention devoted to user-generated content and less-resourced languages. Despite the fact that recent approaches ta...

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Bibliographic Details
Published in:Information (Basel) 2021-12, Vol.12 (12), p.525
Main Authors: Lopes-Cardoso, Henrique, Osório, Tomás Freitas, Barbosa, Luís Vilar, Rocha, Gil, Reis, Luís Paulo, Machado, João Pedro, Oliveira, Ana Maria
Format: Article
Language:English
Subjects:
Artificial intelligence
automated complaint processing
Automation
Classification
Complaints
Customer feedback
decision support
feature analysis
feature engineering
Food safety
Language
low-resourced languages
Machine learning
Natural language processing
Product reviews
Public services
Sentiment analysis
Social networks
User generated content
user-generated text
Citations: Items that this one cites
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Summary:The Natural Language Processing (NLP) community has witnessed huge improvements in the last years. However, most achievements are evaluated on benchmarked curated corpora, with little attention devoted to user-generated content and less-resourced languages. Despite the fact that recent approaches target the development of multi-lingual tools and models, they still underperform in languages such as Portuguese, for which linguistic resources do not abound. This paper exposes a set of challenges encountered when dealing with a real-world complex NLP problem, based on user-generated complaint data in Portuguese. This case study meets the needs of a country-wide governmental institution responsible for food safety and economic surveillance, and its responsibilities in handling a high number of citizen complaints. Beyond looking at the problem from an exclusively academic point of view, we adopt application-level concerns when analyzing the progress obtained through different techniques, including the need to obtain explainable decision support. We discuss modeling choices and provide useful insights for researchers working on similar problems or data.
ISSN:2078-2489
2078-2489
DOI:10.3390/info12120525