Linguistic Understanding of Complaints and Praises in User Reviews

Zhou, Guangyu; Ganesan, Kavita

doi:10.18653/v1/w16-0418

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…In pragmatics, a complaint is defined as "a basic speech act used to express a negative disagreement between reality and expectations for a state, product, organization, or event" [23, p.195-208]. What makes complaints different from negative sentiment polarity is that complaints tend to include expressions of the breaches of the speaker's expectations [26], and include reasons or explanations [31]. The dataset construction is actively conducted to analyse the substance of complaints.…”

Section: Related Workmentioning

confidence: 99%

Complaints with Target Scope Identification on Social Media

Ito,

Murayama,

Yada

et al. 2023

IJCAI

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A complaint is uttered when reality fails to meet one's expectations. Research on complaints, which contributes to our understanding of basic human behavior, has been conducted in the fields of psychology, linguistics, and marketing. Although several approaches have been implemented to the study of complaints, studies have yet focused on a target scope of complaints. Examination of a target scope of complaints is crusial because the functions of complaints, such as evocation of emotion, use of grammar, and intention, are different depending on the target scope. We first tackle the construction and release of a complaint dataset of 6,418 tweets by annotating Japanese texts collected from Twitter with labels of the target scope. Our dataset is available at https://github.com/sociocom/JaGUCHI. We then benchmark the annotated dataset with several machine learning baselines and obtain the best performance of 90.4 F1-score in detecting whether a text was a complaint or not, and a micro-F1 score of 72.2 in identifying the target scope label. Finally, we conducted case studies using our model to demonstrate that identifying a target scope of complaints is useful for sociological analysis.Povzetek: Raziskava se osredotoča na analizo pritožb iz 6.418 tvitov z več metodami strojnega učenja.

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Section: Related Workmentioning

confidence: 99%

Complaints with Target Scope Identification on Social Media

Ito,

Murayama,

Yada

et al. 2023

IJCAI

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“…Most recently, Yang et al (2019) studied customer support dialogues and predicted if these complaints will be escalated with a government agency or made public on social media. To the best of our knowledge, the only previous work that tackles a concept defined as a complaint with computational methods is by Zhou and Ganesan (2016) which studies Yelp reviews. However, they define a complaint as a 'sentence with negative connotation with supplemental information'.…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, the only previous work that tackles a concept defined as a complaint with computational methods is by Zhou and Ganesan (2016) which studies Yelp reviews. However, they define a complaint as a 'sentence with negative connotation with supplemental information'.…”

Section: Related Workmentioning

confidence: 99%

Automatically Identifying Complaints in Social Media

Preoţiuc-Pietro

Găman

Αλέτρας

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Complaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation. Automatically identifying complaints in social media is of utmost importance for organizations or brands to improve the customer experience or in developing dialogue systems for handling and responding to complaints. In this paper, we introduce the first systematic analysis of complaints in computational linguistics. We collect a new annotated data set of written complaints expressed in English on Twitter. 1 We present an extensive linguistic analysis of complaining as a speech act in social media and train strong feature-based and neural models of complaints across nine domains achieving a predictive performance of up to 79 F1 using distant supervision.

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Students Query Classification System

Kumar

Subhakar

Veeresh

2021

IJRTE

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A University or educational institute generally receives a bulk of complaints posted by students every day. The issues relate to their academics or any issues related to their education or related to exam sections etc., because of these bulk of complaints received from the students every day, makes it difficult for the university to sort out them and classify them and send them to their respective departments for resolving the issues. In this project, we work on classifying these complaints based on the classes or departments they belong to, using. By using TF-IDF (term frequency-inverse document frequency) it finds terms which are more related to a specific document by converting to vectors. By capturing some keywords in the complaints, adding some weight to the keywords and using different Machine Learning classification’s we are classifying the complaint based on these keywords. This classification makes the works easier for the university and saves time which is used to sort them and gives better service for the students. Now they can directly send the complaints to the respective departments with ease.

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Linguistic Understanding of Complaints and Praises in User Reviews

Cited by 3 publications

References 12 publications

Complaints with Target Scope Identification on Social Media

Complaints with Target Scope Identification on Social Media

Automatically Identifying Complaints in Social Media

Students Query Classification System

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