Deciphering Review Comments: Identifying Suggestions, Appreciations and Complaints

Pawar, Sachin; Ramrakhiyani, Nitin; Palshikar, Girish Keshav; Hingmire, Swapnil

doi:10.1007/978-3-319-19581-0_18

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…We performed sentence classification to identify strengths, weaknesses and suggestions for improvements found in the supervisor assessments and then used clustering to discover broad categories among them. As this is non-topical classification, we found that SVM with ADWS kernel [16] produced the best results. We also used multi-class multi-label classification techniques to match supervisor assessments to predefined broad perspectives on performance.…”

Section: Conclusion and Further Workmentioning

confidence: 85%

“…We could not find much work related to mining of performance appraisals data. Pawar et al [16] uses kernel-based classification to classify sentences in both performance appraisal text and product reviews into classes SUGGESTION, AP-PRECIATION, COMPLAINT. Apte et al [1] provides two algorithms for matching the descriptions of goals or tasks assigned to employees to a standard template of model goals.…”

Section: Related Workmentioning

confidence: 99%

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Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals

Palshikar

Pawar

Chourasia

et al. 2018

Computational Linguistics and Intelligent Text Processing

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Performance appraisal (PA) is an important HR process to periodically measure and evaluate every employee's performance vis-avis the goals established by the organization. A PA process involves purposeful multi-step multi-modal communication between employees, their supervisors and their peers, such as self-appraisal, supervisor assessment and peer feedback. Analysis of the structured data and text produced in PA is crucial for measuring the quality of appraisals and tracking actual improvements. In this paper, we apply text mining techniques to produce insights from PA text. First, we perform sentence classification to identify strengths, weaknesses and suggestions of improvements found in the supervisor assessments and then use clustering to discover broad categories among them. Next we use multi-class multi-label classification techniques to match supervisor assessments to predefined broad perspectives on performance. Finally, we propose a short-text summarization technique to produce a summary of peer feedback comments for a given employee and compare it with manual summaries. All techniques are illustrated using a real-life dataset of supervisor assessment and peer feedback text produced during the PA of 4528 employees in a large multi-national IT company.

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Section: Conclusion and Further Workmentioning

confidence: 85%

Section: Related Workmentioning

confidence: 99%

Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals

Palshikar

Pawar

Chourasia

et al. 2018

Computational Linguistics and Intelligent Text Processing

Self Cite

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“…Due to the plethora of information available online, companies and retailers find it challenging to identify complaints and address the issues directly. Additionally, detecting complaints on social media entails detecting complaints from unstructured and noisy text snippets with character limitations, usage of random abbreviations, ironical expressions, allegations (Pawar et al 2015), making it a laborious and tedious task. According to an analysis of relevant literature, a multi-modal approach to complaint detection, as opposed to text-based classification, is a new approach.…”

Section: Related Workmentioning

confidence: 99%

Sentiment and Emotion-Aware Multi-Modal Complaint Identification

Singh

Dey

Singha

et al. 2022

AAAI

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The expression of displeasure on a consumer's behalf towards an organization, product, or event is denoted via the speech act known as complaint. Customers typically post reviews on retail websites and various social media platforms about the products or services they purchase, and the reviews may include complaints about the products or services. Automatic detection of consumers' complaints about items or services they buy can be critical for organizations and online merchants since they can use this insight to meet the customers' requirements, including handling and addressing the complaints. Previous studies on Complaint Identification (CI) are limited to text. Images posted with the reviews can provide cues to identify complaints better, thus emphasizing the importance of incorporating multi-modal inputs into the process. Furthermore, the customer's emotional state significantly impacts the complaint expression since emotions generally influence any speech act. As a result, the impact of emotion and sentiment on automatic complaint identification must also be investigated. One of the major contributions of this work is the creation of a new dataset- Complaint, Emotion, and Sentiment Annotated Multi-modal Amazon Reviews Dataset (CESAMARD), a collection of opinionated texts (reviews) and images of the products posted on the website of the retail giant Amazon. We present an attention-based multi-modal, adversarial multi-task deep neural network model for complaint detection to demonstrate the utility of the multi-modal dataset. Experimental results indicate that the multi-modality and multi-tasking complaint identification outperforms uni-modal and single-task variants.

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“…Complaint detection aims to identify a breach of expectations in a given text snippet. However, the use of implicit and ironic expressions and accompaniment of other speech acts such as suggestions, criticism, warnings and threats (Pawar et al, 2015) make it a challenging task. Identifying and classifying complaints automatically is important for: (a) improving customer service chatbots (Coussement and Van den Poel, 2008;Lailiyah et al, 2017;Yang et al, 2019a);(b) linguists to analyze complaint characteristics on large scale (Vásquez, 2011;Kakolaki and Shahrokhi, 2016); and (c) psychologists to understand the behavior of humans that express complaints (Sparks and Browning, 2010).…”

Section: Introductionmentioning

confidence: 99%

Complaint Identification in Social Media with Transformer Networks

Jin¹,

Αλέτρας²

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Complaining is a speech act extensively used by humans to communicate a negative inconsistency between reality and expectations. Previous work on automatically identifying complaints in social media has focused on using feature-based and task-specific neural network models. Adapting state-of-the-art pre-trained neural language models and their combinations with other linguistic information from topics or sentiment for complaint prediction has yet to be explored. In this paper, we evaluate a battery of neural models underpinned by transformer networks which we subsequently combine with linguistic information. Experiments on a publicly available data set of complaints demonstrate that our models outperform previous state-of-the-art methods by a large margin achieving a macro F1 up to 87.

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Deciphering Review Comments: Identifying Suggestions, Appreciations and Complaints

Cited by 6 publications

References 12 publications

Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals

Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals

Sentiment and Emotion-Aware Multi-Modal Complaint Identification

Complaint Identification in Social Media with Transformer Networks

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