Order in Chaos: Prioritizing Mobile App Reviews using Consensus Algorithms

Etaiwi, Layan; Hamel, Sylvie; Guéhéneuc, Yann‐Gaël; Flageol, William; Morales, Rodrigo Rivera

doi:10.1109/compsac48688.2020.0-151

Cited by 4 publications

(2 citation statements)

References 29 publications

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Order By: Relevance

“…In their prioritization algorithm, features of the oldest date, average rating and app category were used to prioritize reviews. However, the prioritization algorithm was not examined in terms of time and accuracy [23].…”

Section: Literature On Classifying Reviewsmentioning

confidence: 99%

Dynamic PScore: A Dynamic Method to Prioritize User Reviews

Dehkordi,

Rastegari,

Najafabadi

et al. 2023

Preprint

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Since the emergence of mobile apps, user reviews have been of great importance for app developers as they contain users’ sentiment, bugs and new requests. Due to the large number of reviews, it is a difficult, time-consuming and error-prone task to prioritize them manually. Having a tool or method for automated prioritization of reviews could save the development team’s time, help application development and maintenance cycle and prevent the development team’s errors. Various methods have been presented for prioritizing reviews, most of which have focused on old features that are no longer valid or ignored new features provided by the store. This study provides a method, called Dynamic PScore, for dynamic prioritization of reviews into five categories of hot, serious, pay attention, getting traction and not serious in the Google Play store. In this method, the score is calculated using ThumbsUp features (popularity of each review), extracting sentiment and considering the number of words in the review and, then, prioritization is done through the obtained score. To experimentally evaluate the proposed method in PPrior database, the corresponding score is calculated for each review and prioritization is done. Finally, the results indicate the presented method has the Accuracy and PSP of 99.94% and 99.86%, respectively, in prioritizing and scoring reviews and its prioritization accuracy has improved by 13.34% compared to recent research.

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Section: Literature On Classifying Reviewsmentioning

confidence: 99%

Dynamic PScore: A Dynamic Method to Prioritize User Reviews

Dehkordi,

Rastegari,

Najafabadi

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The dataset we obtained was unlabeled because the company did not prioritize their customer complaints. Since other information such as ratings of complaints were not available as in previous studies [14], [26], the prioritization was done completely based on the text conveyed.…”

Section: Data Labellingmentioning

confidence: 99%

Comparative Analysis of Classical and Deep Learning-based Natural Language Processing for Prioritizing Customer Complaints

Blümel¹,

Zaki²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Recent advancements in natural language processing have been shown to be very effective for different text mining tasks and thus have provided the opportunity to enhance service research. To improve the customer service experience, this paper compares several natural language processing approaches in order to automatically prioritize incoming customer complaints for service agents. This can help companies to reduce customers' friction and enable effective resource allocations. Our paper uses stateof-the-art feature engineering techniques (e.g., term frequency, TF-IDF and Word2Vec) to identify key words that could enable machine to prioritize complainers. We experimented with many classical machine learning classification algorithms, such as Random Forests, Support Vector Machines, Decision Trees and Logistic Regression, as well as with deep learning-based classifiers, such as convolutional neural networks, bidirectional long short-term memory, and the pre-trained language model BERT to compare the model performance. Our findings show that the pre-trained language model BERT and TF-IDF in combination with Logistic Regression yields the highest macro averaged F1-score across the multiple classes and is therefore most capable of predicting the priority group of incoming customer complaints.

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Elicitation Revisited for More Inclusive Requirements Engineering

Tizard,

Rietz,

Blincoe

2024

Equity, Diversity, and Inclusion in Software Engineering

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James Tizard, Tim Rietz, and Kelly BlincoeTo create inclusive software, development teams need to consider how they identify inclusive requirements for a software product. Requirements elicitation is the first stage in the process of developing the requirements of a software system. Elicitation is about describing the functionality, reliability, efficiency, and usability of the system to be developed, so that it suits the end users’ needs [12].Recent research has found that both traditional elicitation techniques (e.g., user interviews) and newer online crowd-based approaches may have challenges in gathering the views of a diverse set of users. In particular, there are significant challenges in eliciting requirements from users with cognitive disabilities, as well as ensuring that the full demographic spectrum (e.g., by gender, age, ethnicity) of a user base is adequately represented.This chapter discusses the motivations for more inclusive requirements elicitation and the challenges that need to be overcome and finally makes recommendations for both requirements engineering practitioners and researchers.

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Order in Chaos: Prioritizing Mobile App Reviews using Consensus Algorithms

Cited by 4 publications

References 29 publications

Dynamic PScore: A Dynamic Method to Prioritize User Reviews

Dynamic PScore: A Dynamic Method to Prioritize User Reviews

Comparative Analysis of Classical and Deep Learning-based Natural Language Processing for Prioritizing Customer Complaints

Elicitation Revisited for More Inclusive Requirements Engineering

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