Identifying complaints based on semi-supervised mincuts

Singh, Apoorva; Hasanuzzaman, Mohammed; Jangra, Anubhav

doi:10.1016/j.eswa.2021.115668

Cited by 18 publications

(6 citation statements)

References 23 publications

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“…Complaint identification on social media is a timeconsuming and challenging process that needs identifying complaints from disparate and noisy text samples with character constraints, the use of unpredictable abbreviations, and sarcastic expressions. Text-based complaints have earlier been investigated via semi-supervised strategies, complex feature engineering-based machine learning methods, and deep learning models [14], [12], [15]. In the work [12], authors proposed a logistic regression model with hand-crafted features for detecting complaints.…”

Section: Complaint Identificationmentioning

confidence: 99%

Complaint and Severity Identification From Online Financial Content

Singh

Bhatia

Saha

2024

IEEE Trans. Comput. Soc. Syst.

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The automatic detection of financial complaints can benefit businesses and online merchants. Compared to manually tagged complaints, they can use this information to monitor and address issues and effectively route them to appropriate teams. This can also promote greater transparency and accountability when dealing with consumer financial products and services, strengthening the firm's brand value. In linguistic studies, complaints have been classified into severity categories based on the level of risk the complainant is prepared to accept. Furthermore, since emotions influence every speech act, an individual's emotional state considerably impacts the complaint expression. In this paper, we introduce a Financial Complaints resource, a collection of annotated complaints arising between financial institutions and consumers expressed in English on Twitter. The dataset has been enriched with the associated emotion, sentiment, and complaint severity classes. The dataset comprises 3149 complaint and 3133 non-complaint instances spanning over ten domains (e.g., credit cards, mortgages, etc.). For a comprehensive evaluation of our dataset, we develop a multi-task framework for complaint detection and severity classification with emotion recognition and sentiment classification as the additional tasks and compare it with several existing baselines 1 .

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Section: Complaint Identificationmentioning

confidence: 99%

Complaint and Severity Identification From Online Financial Content

Singh

Bhatia

Saha

2024

IEEE Trans. Comput. Soc. Syst.

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“…Complaints are de ned as reports from consumers to provide information regarding product or service problems. The term complaint implies a feeling of resentment or dissatisfaction that consumers feel toward the person, product, or organization that is responsible [7]. Public service providers, both private and government, can be the target of complaints.…”

Section: Related Workmentioning

confidence: 99%

“…Over time, many complaints are coming online through social media platforms, generating many complaint texts. Fast identi cation of consumer complaint text using natural language processing can help public service providers to analyze and handle consumer complaints [7].…”

Section: Related Workmentioning

confidence: 99%

“…Several previous studies have carried out a complaint monitoring system from Twitter data, such as a complaints monitoring system for farmers using the machine learning method [5] or a complaint monitoring system for public services [6], [7]. Nevertheless, there has not been much research related to monitoring systems that extract information from a complaint tweet to identify the location entity where the complaint occurred.…”

Section: Introductionmentioning

confidence: 99%

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Transformer-based Information Extraction from Twitter Text on Complaint Monitoring System

Purwitasari,

Fatichah,

Ariyanto

et al. 2023

Preprint

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Customer complaints receive more attention from a company because bad handling of complaints will cause the loss of existing customers. Current customers tend to use social media to report complaints by including information on the location of the complaint so that the location also needs to be extracted because it can be authentic geographic evidence in the complaint monitoring system. The number of complaint reports via social media is increasing occasionally, making the manual complaint monitoring system very inefficient because it takes a long time. This study proposes an automatic complaint monitoring system on Indonesian text from Twitter by extracting transformer-based information. The automatic complaint monitoring system uses a combination of the Bidirectional Encoder Representation from Transformer (BERT) model to extract location information on complaint tweets and the Convolutional Neural Network (CNN) model to classify the complaint type, which gets the highest F1 score of 0.90471. The complaint monitoring system is also visualized in the complaint locations to make it easier for companies to find the location of complaint reports, and action can be taken to deal with public complaints immediately.

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“…Several previous studies have classified complaints but carried out a sentiment analysis on complaints' text using a semi-supervised min cuts algorithm. It classifies the text of complaints/community reports into two classes, namely reports and non-reports, with an accuracy of 83.8% [11]. Meanwhile, in this study, the Naïve Bayes Classifier algorithm will be applied to classify complaints/reports into three classes: simple reports, medium reports, and heavy reports involving 5 (five) attributes.…”

Section: Introductionmentioning

confidence: 99%

Application of The Naïve Bayes Classifier Algorithm to Classify Community Complaints

Wabang¹,

Nurhayati²,

Farikhin³

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Unsatisfactory public services encourage the public to submit complaints/ reports to public service providers to improve their services. However, each complaint/ report submitted varies. Therefore, the first step of the community complaint resolution process is to classify every incoming community complaint. The Ombudsman of The Republic of Indonesia annually receives a minimum of 10,000 complaints with an average of 300-500 reports per province per year, classifies complaints/ community reports to divide them into three classes, namely simple reports, medium reports, and heavy reports. The classification process is carried out using a weight assessment of each complaint/ report using 5 (five) attributes. It becomes a big job if done manually. This impacts the inefficiency of the performance time of complaint management officers. As an alternative solution, in this study, a machine learning method with the Naïve Bayes Classifier algorithm was applied to facilitate the process of automatically classifying complaints/ community reports to be more effective and efficient. The results showed that the classification of complaints/ community reports by applying the Naïve Bayes Classifier algorithm gives a high accuracy value of 92%. In addition, the average precision, recall, and f1-score values, respectively, are 91%, 93%, and 92%.

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Identifying complaints based on semi-supervised mincuts

Cited by 18 publications

References 23 publications

Complaint and Severity Identification From Online Financial Content

Complaint and Severity Identification From Online Financial Content

Transformer-based Information Extraction from Twitter Text on Complaint Monitoring System

Application of The Naïve Bayes Classifier Algorithm to Classify Community Complaints

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