Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review

Pradeep Kumar Roy, Sunil Saumya, Jyoti Prakash Singh, Snehasish Banerjee, Adnan Gutub

Research output: Contribution to journalArticlepeer-review

Abstract

Over the last couple of decades, community question-answering sites (CQAs) have been a topic of much academic interest. Scholars have often leveraged traditional machine learning (ML) and deep learning (DL) to explore the ever-growing volume of content that CQAs engender. To clarify the current state of the CQA literature that has used ML and DL, this paper reports a systematic literature review. The goal is to summarise and synthesise the major themes of CQA research related to (i) questions, (ii) answers and (iii) users. The final review included 133 articles. Dominant research themes include question quality, answer quality, and expert identification. In terms of dataset, some of the most widely studied platforms include Yahoo! Answers, Stack Exchange and Stack Overflow. The scope of most articles was confined to just one platform with few cross-platform investigations. Articles with ML outnumber those with DL. Nonetheless, the use of DL in CQA research is on an upward trajectory. A number of research directions are proposed.
Original languageEnglish
Number of pages23
JournalCAAI Transactions on Intelligence Technology
DOIs
Publication statusPublished - 4 May 2022

Bibliographical note

© 2022The Authors.CAAI Transactionson IntelligenceTechnologypublishedby John Wiley& SonsLtd on behalfof The Institutionof Engineeringand Technology and ChongqingUniversityof Technology.

Keywords

  • answer quality
  • community question answering
  • deep learning
  • expert user
  • Machine Learning
  • question quality

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