Towards Automated Taxonomy Generation for Grouping App Reviews: A Preliminary Empirical Study

Malgaonkar, Saurabh; Licorish, Sherlock A.; Savarimuthu, Bastin Tony Roy

doi:10.1007/978-3-030-58793-2_10

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…These snippets, represented in VSM or BoW, are compared using similarity measure like Cosine similarity (Vu et al 2015a;Shams et al 2020), Dice similarity coefficient (Palomba et al 2015;Zhou et al 2020) or Jaccard index (Iacob et al 2016). These techniques support Searching and Information Retrieval e.g., to link reviews with issue reports from issue tracking systems , Recommendation e.g., to recommend review responses based on old ones that have been posted to similar reviews (Greenheld et al 2018), Clustering e.g., to group semantically similar user opinions (Vu et al 2016;Malgaonkar et al 2020), and Content Analysis e.g., to compare review content (Malavolta et al 2015a).…”

Section: Natural Language Processingmentioning

confidence: 99%

“…Co-occurrences may be insufficient as phrases such as 'all the' may co-occur frequently but are not meaningful. Hence, primary studies explore several methods to filter out the most meaningful collocations, such as Pointwise Mutual Information (PMI) (Gao et al 2018b;Malgaonkar et al 2020) and hypothesis testing (Jurafsky and Martin 2009;Guzman and Maalej 2014;Da ¸browski et al 2020).…”

Section: Natural Language Processingmentioning

confidence: 99%

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Analysing app reviews for software engineering: a systematic literature review

et al. 2022

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App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.

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Section: Natural Language Processingmentioning

confidence: 99%

Section: Natural Language Processingmentioning

confidence: 99%

Analysing app reviews for software engineering: a systematic literature review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Hence, during testing and designing of IMCSC, we apply the Pareto Principle (80:20 rule). The main idea is that the chatbot should respond well to the 20% of the highly repetitive use cases that are more than 80% of the volume of use cases [10]. For the rest of the use cases, we cover it with the default fallback intent which responds to all the improper user inputs.…”

Section: Whatsapp Integrationmentioning

confidence: 99%

Intent Matching based Customer Services Chatbot with Natural Language Understanding

Chaidrata

Imtha

Chew

et al. 2021

2021 5th International Conference on Communication and Information Systems (ICCIS)

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Customer service is the lifeblood of any business. Excellent customer service not only generates return business but also creates new customers. Looking at the demanding market to provide a 24/7 service to customers, many organisations are increasingly engaged in popular social media and text messaging platforms such as WhatsApp and Facebook Messenger in providing a 24/7 service to customers in the current demanding market. In this paper, we present an intent matching based customer services chatbot (IMCSC), which is capable of replacing the customer service work of sales personnel, whilst interacting in a more natural and human-like manner through the employment of Natural Language Understanding (NLU). The bot is able to answer the most common frequently asked questions and we have also integrated features for the processing and exporting of customer orders to a Google Sheet.

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Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools

Massenon,

Gambo,

Ogundokun

et al. 2024

PeerJ Computer Science

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Mobile app reviews are valuable for gaining user feedback on features, usability, and areas for improvement. Analyzing these reviews manually is difficult due to volume and structure, leading to the need for automated techniques. This mapping study categorizes existing approaches for automated and semi-automated tools by analyzing 180 primary studies. Techniques include topic modeling, collocation finding, association rule-based, aspect-based sentiment analysis, frequency-based, word vector-based, and hybrid approaches. The study compares various tools for analyzing mobile app reviews based on performance, scalability, and user-friendliness. Tools like KEFE, MERIT, DIVER, SAFER, SIRA, T-FEX, RE-BERT, and AOBTM outperformed baseline tools like IDEA and SAFE in identifying emerging issues and extracting relevant information. The study also discusses limitations such as manual intervention, linguistic complexities, scalability issues, and interpretability challenges in incorporating user feedback. Overall, this mapping study outlines the current state of feature extraction from app reviews, suggesting future research and innovation opportunities for extracting software requirements from mobile app reviews, thereby improving mobile app development.

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Towards Automated Taxonomy Generation for Grouping App Reviews: A Preliminary Empirical Study

Cited by 3 publications

References 20 publications

Analysing app reviews for software engineering: a systematic literature review

Analysing app reviews for software engineering: a systematic literature review

Intent Matching based Customer Services Chatbot with Natural Language Understanding

Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools

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